14.2.20 Support Vector Machines, SVM

Chapter Contents (Back)
Support Vector Machines. SVM. Heavily referenced in the face recognition literature. Those are in the face recognition sections. Subsections are somewhat arbitrary.
See also Training Support Vector Machines, SVM Training, Learning. Specific applications in other sections.
See also Support Vector Machines, SVM, Applied to Recognition.
See also Support Vector Machines, SVM, Incremental, Multi-Step.

Chen, S., Gunn, S.R., Harris, C.J.,
Decision feedback equaliser design using support vector machines,
VISP(147), No. 3, 2000, pp. 213-219. 0008
BibRef

Dhanjal, C.[Charanpal], Gunn, S.R.[Steve R.], Shawe-Taylor, J.[John],
Efficient Sparse Kernel Feature Extraction Based on Partial Least Squares,
PAMI(31), No. 8, August 2009, pp. 1347-1361.
IEEE DOI 0906
Dealing with irrelevant features in classificaton. BibRef

Drezet, P.M.L.[Pierre M.L.], Harrison, R.F.[Robert F.],
A new method for sparsity control in support vector classification and regression,
PR(34), No. 1, January 2001, pp. 111-125.
Elsevier DOI 0010
BibRef

Mangasarian, O.L.[Olvi L.], Musicant, D.R.[David R.],
Robust Linear and Support Vector Regression,
PAMI(22), No. 9, September 2000, pp. 950-955.
IEEE DOI 0010
BibRef

Mangasarian, O.L.[Olvi L.], Wild, E.W.[Edward W.],
Multisurface Proximal Support Vector Machine Classification via Generalized Eigenvalues,
PAMI(28), No. 1, January 2006, pp. 69-74.
IEEE DOI 0512
BibRef

Pedroso, J.P.[João Pedro], Murata, N.[Noboru],
Support Vector Machines with Different Norms: Motivation, Formulations and Results,
PRL(22), No. 12, October 2001, pp. 1263-1272.
Elsevier DOI 0108
BibRef

Guillamet, D.[David], Vitrià, J.[Jordi],
Discriminant Local Regions Using Support Vector Machines,
ELCVIA(1), 2002, pp. None. Reference to this is wrong. See conference paper. 0206

Earlier:
Local Discriminant Regions Using Support Vector Machines for Object Recognition,
SSSPR00(559-559).
Springer DOI BibRef

Zhou, W.D.[Wei-Da], Zhang, L.[Li], Jiao, L.C.[Li-Cheng],
Linear programming support vector machines,
PR(35), No. 12, December 2002, pp. 2927-2936.
Elsevier DOI 0209
BibRef

Zhang, L.[Li], Zhou, W.D.[Wei-Da], Jiao, L.C.[Li-Cheng],
Wavelet Support Vector Machine,
SMC-B(34), No. 1, February 2004, pp. 34-39.
IEEE Abstract. 0403
BibRef

Zhang, L.[Li], Zhou, W.D.[Wei-Da],
Density-induced margin support vector machines,
PR(44), No. 7, July 2011, pp. 1448-1460.
Elsevier DOI 1103
Support vector machine; Maximum margin classifier; Machine learning; Relative density degree BibRef

Chua, K.S.[Kok Seng],
Efficient computations for large least square support vector machine classifiers,
PRL(24), No. 1-3, January 2003, pp. 75-80.
Elsevier DOI 0211
BibRef

Davy, M., Gretton, A., Doucet, A., Rayner, P.J.W.,
Optimized support vector machines for nonstationary signal classification,
SPLetters(9), No. 12, December 2002, pp. 442-445.
IEEE Top Reference. 0301
BibRef

Parrado-Hernández, E.[Emilio], Mora-Jiménez, I., Arenas-García, J., Figueiras-Vidal, A.R., Navia-Vázquez, A.,
Growing support vector classifiers with controlled complexity,
PR(36), No. 7, July 2003, pp. 1479-1488.
Elsevier DOI 0304
BibRef

García-García, D.[Darío], Parrado Hernández, E.[Emilio], Díaz-de María, F.[Fernando],
A New Distance Measure for Model-Based Sequence Clustering,
PAMI(31), No. 7, July 2009, pp. 1325-1331.
IEEE DOI 0905
based on the Kullback-Leibler divergence. BibRef

Garcia-Garcia, D.[Dario], Parrado-Hernandez, E.[Emilio], Diaz-de-Maria, F.[Fernando],
State-space dynamics distance for clustering sequential data,
PR(44), No. 5, May 2011, pp. 1014-1022.
Elsevier DOI 1101
Sequential data; Clustering; Hidden Markov models BibRef

Muñoz-Romero, S.[Sergio], Gómez-Verdejo, V.[Vanessa], Parrado-Hernández, E.[Emilio],
A novel framework for parsimonious multivariate analysis,
PR(71), No. 1, 2017, pp. 173-186.
Elsevier DOI 1707
Feature, selection BibRef

Fei, Y.N., Lu, Z., Tang, W.H., Wu, Q.H.,
Harmonic Estimation Using a Global Search Optimiser,
EvoIASP07(261-270).
Springer DOI 0704
BibRef

Lau, K.W., Wu, Q.H.,
Local prediction of non-linear time series using support vector regression,
PR(41), No. 5, May 2008, pp. 1556-1564.
Elsevier DOI 0711
Time series analysis; Local prediction; Support vector regression; Radial basis function; Least square; Delay coordinates; State space reconstruction BibRef

Chen, Y.S.[Yi-Song], Wang, G.P.[Guo-Ping], Dong, S.H.[Shi-Hai],
Learning with progressive transductive support vector machine,
PRL(24), No. 12, August 2003, pp. 1845-1855.
Elsevier DOI 0304
BibRef

Steinwart, I.[Ingo],
On the optimal parameter choice for v-support vector machines,
PAMI(25), No. 10, October 2003, pp. 1274-1284.
IEEE Abstract. 0310

See also New Support Vector Algorithms. The parameter v should be twice the optimal Bayes risk. BibRef

Rojo Alvarez, J.L., Martinez Ramon, M., Figueiras Vidal, A.R., Garcia Armada, A., Artes Rodriguez, A.,
A robust support vector algorithm for nonparametric spectral analysis,
SPLetters(10), No. 11, November 2003, pp. 320-323.
IEEE Abstract. 0310
BibRef

Maruyama, K.I.[Ken-Ichi], Maruyama, M.[Minoru], Miyao, H.[Hidetoshi], Nakano, Y.[Yasuaki],
A method to make multiple hypotheses with high cumulative recognition rate using SVMs,
PR(37), No. 2, February 2004, pp. 241-251.
Elsevier DOI 0311
BibRef

Karaçali, B.[Bilge], Ramanath, R.[Rajeev], Snyder, W.E.[Wesley E.],
A comparative analysis of structural risk minimization by support vector machines and nearest neighbor rule,
PRL(25), No. 1, January 2004, pp. 63-71.
Elsevier DOI 0311
BibRef

Mitra, P.[Pabitra], Murthy, C.A., Pal, S.K.[Sankar K.],
A Probabilistic Active Support Vector Learning Algorithm,
PAMI(26), No. 3, March 2004, pp. 413-418.
IEEE Abstract. 0402
Rather than points based on proximity to the separating hyperplane, use points according to a distribution determined by the hyperplane and confidence factor. BibRef

Chen, J.H.[Jiun-Hung], Chen, C.S.[Chu-Song],
Reducing SVM Classification Time Using Multiple Mirror Classifiers,
SMC-B(34), No. 2, April 2004, pp. 1173-1183.
IEEE Abstract. 0404
BibRef
Earlier:
Speeding up SVM decision based on mirror points,
ICPR02(II: 869-872).
IEEE DOI 0211
BibRef

Lee, J.W.[Jae-Wook], Lee, D.W.[Dae-Won],
An Improved Cluster Labeling Method for Support Vector Clustering,
PAMI(27), No. 3, March 2005, pp. 461-464.
IEEE Abstract. 0501
BibRef

Lee, J.W.[Jae-Wook], Lee, D.W.[Dae-Won],
Dynamic Characterization of Cluster Structures for Robust and Inductive Support Vector Clustering,
PAMI(28), No. 11, November 2006, pp. 1869-1874.
IEEE DOI 0609
BibRef

Lee, D.W.[Dae-Won], Lee, J.W.[Jae-Wook],
Domain described support vector classifier for multi-classification problems,
PR(40), No. 1, January 2007, pp. 41-51.
Elsevier DOI 0611
Multi-class classification; Kernel methods; Bayes decision theory; Density estimation; Support vector domain description BibRef

Jung, K.H.[Kyu-Hwan], Lee, D.W.[Dae-Won], Lee, J.W.[Jae-Wook],
Fast support-based clustering method for large-scale problems,
PR(43), No. 5, May 2010, pp. 1975-1983.
Elsevier DOI 1003
Large-scale problem; Kernel methods; Support vector clustering; Cluster labeling; Dynamical system BibRef

Lee, K.Y.[Ki-Young], Kim, D.W.[Dae-Won], Lee, K.H.[Kwang H.], Lee, D.[Doheon],
Possibilistic support vector machines,
PR(38), No. 8, August 2005, pp. 1325-1327.
Elsevier DOI 0505
BibRef

Ayat, N.E., Cheriet, M., Suen, C.Y.,
Automatic model selection for the optimization of SVM kernels,
PR(38), No. 10, October 2005, pp. 1733-1745.
Elsevier DOI 0508
BibRef

Adankon, M.M.[Mathias M.], Cheriet, M.[Mohamed],
Optimizing resources in model selection for support vector machine,
PR(40), No. 3, March 2007, pp. 953-963.
Elsevier DOI 0611
Model selection; SVM; Kernel; Hyperparameters; Optimizing time
See also Help-Training for semi-supervised support vector machines. BibRef

Zhang, J.Y.[Jia-Yong], Liu, Y.X.[Yan-Xi],
SVM decision boundary based discriminative subspace induction,
PR(38), No. 10, October 2005, pp. 1746-1758.
Elsevier DOI 0508
BibRef

González, L., Angulo, C., Velasco, F., Català, A.,
Unified dual for bi-class SVM approaches,
PR(38), No. 10, October 2005, pp. 1772-1774.
Elsevier DOI 0508
BibRef
Earlier: More developed version:
Dual unification of bi-class support vector machine formulations,
PR(39), No. 7, July 2006, pp. 1325-1332.
Elsevier DOI 0606
Large margin principle; Optimization; Convex hull BibRef

Lee, K.Y.[Ki-Young], Kim, D.W.[Dae-Won], Lee, D.[Doheon], Lee, K.H.[Kwang H.],
Improving support vector data description using local density degree,
PR(38), No. 10, October 2005, pp. 1768-1771.
Elsevier DOI 0508
BibRef

Asharaf, S., Shevade, S.K., Murty, M.N.[M. Narasimha],
Rough support vector clustering,
PR(38), No. 10, October 2005, pp. 1779-1783.
Elsevier DOI 0508

See also Rough set based incremental clustering of interval data. BibRef

Reddy, I.S.[I. Sathish], Shevade, S.K.[Shirish K.], Murty, M.N.,
A fast quasi-Newton method for semi-supervised SVM,
PR(44), No. 10-11, October-November 2011, pp. 2305-2313.
Elsevier DOI 1101
Semi-supervised learning; Support vector machines; Quasi-Newton methods; Nonconvex optimization BibRef

Asharaf, S., Murty, M.N.[M. Narasimha],
Scalable non-linear Support Vector Machine using hierarchical clustering,
ICPR06(I: 908-911).
IEEE DOI 0609
BibRef

Nath, J.S.[J. Saketha], Shevade, S.K.,
An efficient clustering scheme using support vector methods,
PR(39), No. 8, August 2006, pp. 1473-1480.
Elsevier DOI Clustering; Support vector machines; R*-tree 0606
BibRef

El-Yaniv, R.[Ran], Gerzon, L.[Leonid],
Effective transductive learning via objective model selection,
PRL(26), No. 13, 1 October 2005, pp. 2104-2115.
Elsevier DOI 0509
BibRef

Lauer, F.[Fabien], Bloch, G.[Gérard],
Ho-Kashyap classifier with early stopping for regularization,
PRL(27), No. 9, July 2006, pp. 1037-1044.
Elsevier DOI 0605
Early stopping; Robustness; SVM
See also Algorithm for Linear Inequalities and its Applications, An. BibRef

Li, M.K.[Ming-Kun], Sethi, I.K.[Ishwar K.],
Confidence-Based Active Learning,
PAMI(28), No. 8, August 2006, pp. 1251-1261.
IEEE DOI 0606
Identify the uncertain samples. BibRef

Li, M.K.[Ming-Kun], Sethi, I.K.[Ishwar K.],
Confidence-based classifier design,
PR(39), No. 7, July 2006, pp. 1230-1240.
Elsevier DOI 0606
BibRef
Earlier:
SVM-based classifier design with controlled confidence,
ICPR04(I: 164-167).
IEEE DOI 0409
Confidence-based classification; Error estimation; Reject option; Dynamic bin width allocation BibRef

Liu, Y.G.[Yi-Guang], You, Z.S.[Zhi-Sheng], Cao, L.P.[Li-Ping],
A novel and quick SVM-based multi-class classifier,
PR(39), No. 11, November 2006, pp. 2258-2264.
Elsevier DOI 0608
Multi-class classifier; SVMlight approach; Objective function BibRef

Li, Q.[Qing], Jiao, L.C.[Li-Cheng], Hao, Y.J.[Ying-Juan],
Adaptive simplification of solution for support vector machine,
PR(40), No. 3, March 2007, pp. 972-980.
Elsevier DOI 0611
Support vector machine; Simplification; Vector correlation; Feature vector; Regression estimation; Pattern recognition BibRef

Han, Y., Lam, W.[Wai], Ling, C.X.[Charles X.],
Customized Generalization of Support Patterns for Classification,
SMC-B(36), No. 6, December 2006, pp. 1306-1318.
IEEE DOI 0701
BibRef

Jayadeva, Khemchandani, R., Chandra, S.[Suresh],
Twin Support Vector Machines for Pattern Classification,
PAMI(29), No. 5, May 2007, pp. 905-910.
IEEE DOI 0704
A binary SVM classifier that determines two nonparallel planes by solving two related SVM-type problems. BibRef

Khemchandani, R., Goyal, K., Chandra, S.[Suresh],
Twin Support Vector Machine based Regression,
ICAPR15(1-6)
IEEE DOI 1511
quadratic programming BibRef

Jayadeva, Shah, S.[Sameena], Chandra, S.[Suresh],
Kernel Optimization Using a Generalized Eigenvalue Approach,
PReMI09(32-37).
Springer DOI 0912
BibRef

Jayadeva, Shah, S.[Sameena], Chandra, S.[Suresh],
Zero Norm Least Squares Proximal SVR,
PReMI09(38-43).
Springer DOI 0912
BibRef

Qiao, H.[Hong], Wang, Y.G.[Yan-Guo], Zhang, B.[Bo],
A simple decomposition algorithm for support vector machines with polynomial-time convergence,
PR(40), No. 9, September 2007, pp. 2543-2549.
Elsevier DOI 0705
Support vector machines; Decomposition methods; Convergence; Statistical learning theory; Pattern recognition BibRef

Wang, D.[Di], Zhang, B.[Bo], Zhang, P.[Peng], Qiao, H.[Hong],
An online core vector machine with adaptive MEB adjustment,
PR(43), No. 10, October 2010, pp. 3468-3482.
Elsevier DOI 1007
Minimum enclosing ball; Online classifier; Core vector machine; Support vector machine; Machine learning BibRef

Doumpos, M., Zopounidis, C., Golfinopoulou, V.,
Additive Support Vector Machines for Pattern Classification,
SMC-B(37), No. 3, June 2007, pp. 540-550.
IEEE DOI 0706
BibRef

Chuang, C.C.,
Fuzzy Weighted Support Vector Regression With a Fuzzy Partition,
SMC-B(37), No. 3, June 2007, pp. 630-640.
IEEE DOI 0706
BibRef

Tian, S.F.[Sheng-Feng], Mu, S.M.[Shao-Min], Yin, C.H.[Chuan-Huan],
Length-weighted string kernels for sequence data classification,
PRL(28), No. 13, 1 October 2007, pp. 1651-1656.
Elsevier DOI 0709
Support vector machine; String kernel; Classification BibRef

Zingman, I.[Igor], Meir, R.[Ron], El-Yaniv, R.[Ran],
Size-density spectra and their application to image classification,
PR(40), No. 12, December 2007, pp. 3336-3348.
Elsevier DOI 0709
Image classification; Algebraic opening; Density opening; Rank-max opening; Pattern size spectrum; Pattern density spectrum; Pattern size-density spectrum; Size-density signature; Support vector machine BibRef

Bayro-Corrochano, E.[Eduardo], Arana-Daniel, N.[Nancy],
Theory and Applications of Clifford Support Vector Machines,
JMIV(28), No. 1, May 2007, pp. 29-46.
Springer DOI 0710
BibRef

López-González, G., Arana-Daniel, N.[Nancy], Bayro-Corrochano, E.[Eduardo],
Quaternion Support Vector Classifier,
CIARP14(722-729).
Springer DOI 1411
BibRef

Ye, W.[Wang], Huang, S.T.[Shang-Teng],
Reducing the number of sub-classifiers for pairwise multi-category support vector machines,
PRL(28), No. 15, 1 November 2007, pp. 2088-2093.
Elsevier DOI 0711
SVM; Multi-category classification; Pairwise; Uncertainty sampling BibRef

Zafeiriou, S.P., Tefas, A., Pitas, I.,
Minimum Class Variance Support Vector Machines,
IP(16), No. 10, October 2007, pp. 2551-2564.
IEEE DOI 0711
BibRef

Vretos, N., Tefas, A., Pitas, I.,
Using robust dispersion estimation in support vector machines,
PR(46), No. 12, 2013, pp. 3441-3451.
Elsevier DOI 1308
Support vector machines BibRef

Zhou, S.S.[Shui-Sheng], Liu, H.W.[Hong-Wei], Zhou, L.H.[Li-Hua], Ye, F.[Feng],
Semismooth Newton support vector machine,
PRL(28), No. 15, 1 November 2007, pp. 2054-2062.
Elsevier DOI 0711
Support vector machines; Semismooth; Lagrangian dual; Cholesky factorization BibRef

Astorino, A.[Annabella], Fuduli, A.[Antonio],
Nonsmooth Optimization Techniques for Semisupervised Classification,
PAMI(29), No. 12, December 2007, pp. 2135-2142.
IEEE DOI 0711
Transductive Support Vector Machine. BibRef

Guo, G.[Gao], Zhang, J.S.[Jiang-She],
Reducing examples to accelerate support vector regression,
PRL(28), No. 16, December 2007, pp. 2173-2183.
Elsevier DOI 0711
Support vector machine; Support vector regression; Data reduced method; Cross validation; k-Nearest neighbor BibRef

Kang, W.S.[Woo-Sung], Choi, J.Y.[Jin Young],
Domain density description for multiclass pattern classification with reduced computational load,
PR(41), No. 6, June 2008, pp. 1997-2009.
Elsevier DOI 0802
Multiclass pattern classification; Computational load reduction; Support vector learning BibRef

Li, D.F.[Ding-Fang], Hu, W.C.[Wen-Chao], Xiong, W.[Wei], Yang, J.B.[Jin-Bo],
Fuzzy relevance vector machine for learning from unbalanced data and noise,
PRL(29), No. 9, 1 July 2008, pp. 1175-1181.
Elsevier DOI 0711
Relevance vector machine; Unbalanced data; Noise; Fuzzy membership; Bayesian inference BibRef

Kumar, M.A.[M. Arun], Gopal, M.,
Application of smoothing technique on twin support vector machines,
PRL(29), No. 13, 1 October 2008, pp. 1842-1848.
Elsevier DOI 0804
Support vector machines; Pattern recognition; Twin support vector machines BibRef

Kumar, M.A.[M. Arun], Gopal, M.,
A comparison study on multiple binary-class SVM methods for unilabel text categorization,
PRL(31), No. 11, 1 August 2010, pp. 1437-1444.
Elsevier DOI 1008
Multiclass classification; One-against-all; One-against-one; Text categorization; Support vector machines (SVMs) BibRef

Yin, J.S.[Jun-Song], Hu, D.[Dewen], Zhou, Z.T.[Zong-Tan],
Noisy manifold learning using neighborhood smoothing embedding,
PRL(29), No. 11, 1 August 2008, pp. 1613-1620.
Elsevier DOI 0804
Neighbor smoothing embedding (NSE); Manifold learning; Locally linear embedding (LLE); Local linear surface estimator BibRef

Guo, S.M., Chen, L.C., Tsai, J.S.H.,
A boundary method for outlier detection based on support vector domain description,
PR(42), No. 1, January 2009, pp. 77-83.
Elsevier DOI 0809
Outlier detection; Support vector domain description BibRef

Wang, L., Xue, P., Chan, K.L.,
Two Criteria for Model Selection in Multiclass Support Vector Machines,
SMC-B(38), No. 6, December 2008, pp. 1432-1448.
IEEE DOI 0812
BibRef

Wu, K.P.[Kuo-Ping], Wang, S.D.[Sheng-De],
Choosing the kernel parameters for support vector machines by the inter-cluster distance in the feature space,
PR(42), No. 5, May 2009, pp. 710-717.
Elsevier DOI 0902
SVM; Support vector machines; Kernel parameters; Inter-cluster distances BibRef

Zhao, Y.P.[Yong-Ping], Sun, J.G.[Jian-Guo],
Recursive reduced least squares support vector regression,
PR(42), No. 5, May 2009, pp. 837-842.
Elsevier DOI 0902
Least squares support vector regression; Reduced technique; Iterative strategy; Parsimoniousness; Classification BibRef

Filippi, A.M., Archibald, R.,
Support Vector Machine-Based Endmember Extraction,
GeoRS(47), No. 3, March 2009, pp. 771-791.
IEEE DOI 0903
BibRef

Liang, X.[Xun], Wang, C.[Chao],
Separating hypersurfaces of SVMs in input spaces,
PRL(30), No. 5, 1 April 2009, pp. 469-476.
Elsevier DOI 0903
Separating hyperplane; Separating hypersurface; Input sample space; High-dimensional feature space; Support vector machine BibRef

Mu, T., Nandi, A.K.[Asoke K.],
Multiclass Classification Based on Extended Support Vector Data Description,
SMC-B(39), No. 5, October 2009, pp. 1206-1216.
IEEE DOI 0906
BibRef

Chen, G.Y.[Guang-Yi], Dudek, G.[Gregory],
Auto-correlation wavelet support vector machine,
IVC(27), No. 8, 2 July 2009, pp. 1040-1046.
Elsevier DOI 0906
BibRef
Earlier:
Auto-Correlation Wavelet Support Vector Machine and Its Applications to Regression,
CRV05(246-252).
IEEE DOI 0505
Wavelets; Support vector machine; Machine learning; Pattern recognition; Function regression; Auto-correlation BibRef

Chen, J., Wang, C., Wang, R.,
Using Stacked Generalization to Combine SVMs in Magnitude and Shape Feature Spaces for Classification of Hyperspectral Data,
GeoRS(47), No. 7, July 2009, pp. 2193-2205.
IEEE DOI 0906
BibRef

Peleg, D.[Dori], Meir, R.[Ron],
A sparsity driven kernel machine based on minimizing a generalization error bound,
PR(42), No. 11, November 2009, pp. 2607-2614.
Elsevier DOI 0907
Sparsity; Classification; Generalization error bounds; Statistical learning theory BibRef

Tuia, D., Pacifici, F.[Fabio], Kanevski, M., Emery, W.J.[William J.],
Classification of Very High Spatial Resolution Imagery Using Mathematical Morphology and Support Vector Machines,
GeoRS(47), No. 11, November 2009, pp. 3866-3879.
IEEE DOI 0911

See also Comparing Statistical and Neural Network Methods Applied to Very High Resolution Satellite Images Showing Changes in Man-Made Structures at Rocky Flats. BibRef

Zhao, W.Z.[Wen-Zhi], Du, S.H.[Shi-Hong], Wang, Q.[Qiao], Emery, W.J.[William J.],
Contextually guided very-high-resolution imagery classification with semantic segments,
PandRS(132), No. 1, 2017, pp. 48-60.
Elsevier DOI 1710
VHR, images BibRef

Tuia, D., Camps-Valls, G., Matasci, G., Kanevski, M.,
Learning Relevant Image Features with Multiple-Kernel Classification,
GeoRS(48), No. 10, October 2010, pp. 3780-3791.
IEEE DOI 1003
BibRef

Volpi, M., Tuia, D., Kanevski, M.,
Memory-Based Cluster Sampling for Remote Sensing Image Classification,
GeoRS(50), No. 8, August 2012, pp. 3096-3106.
IEEE DOI 1208
BibRef

Matasci, G., Volpi, M., Kanevski, M., Bruzzone, L., Tuia, D.,
Semisupervised Transfer Component Analysis for Domain Adaptation in Remote Sensing Image Classification,
GeoRS(53), No. 7, July 2015, pp. 3550-3564.
IEEE DOI 1503
Feature extraction BibRef

Huang, X., Shi, L., Suykens, J.A.K.[Johan A.K.],
Support Vector Machine Classifier With Pinball Loss,
PAMI(36), No. 5, May 2014, pp. 984-997.
IEEE DOI 1405
Fasteners BibRef

Deselaers, T.[Thomas], Heigold, G.[Georg], Ney, H.[Hermann],
Object classification by fusing SVMs and Gaussian mixtures,
PR(43), No. 7, July 2010, pp. 2476-2484.
Elsevier DOI 1003
BibRef
Earlier:
SVMs, Gaussian mixtures, and their generative/discriminative fusion,
ICPR08(1-4).
IEEE DOI 0812
Support vector machine; Gaussian mixtures; Discriminative classifiers; Generative classifiers; Local-feature-based object recognition BibRef

Weyand, T.[Tobias], Deselaers, T.[Thomas], Ney, H.[Hermann],
Log-linear Mixtures for Object Class Recognition,
BMVC09(xx-yy).
PDF File. 0909
BibRef

Muñoz, A.[Alberto], González, J.[Javier],
Representing Functional Data Using Support Vector Machines,
PRL(31), No. 6, 15 April 2010, pp. 511-516.
Elsevier DOI 1004
BibRef
Earlier: A2, A1: CIARP08(332-339).
Springer DOI 0809
Functional Data Analysis (FDA); Kernel methods; Support vector machines; Cluster; Classification BibRef

Muñoz, A.[Alberto], González, J.[Javier], de Diego, I.M.[Isaac Martín],
Local Linear Approximation for Kernel Methods: The Railway Kernel,
CIARP06(936-944).
Springer DOI 0611
BibRef

Moguerza, J.M.[Javier M.], Muñoz, A.[Alberto], de Diego, I.M.[Isaac Martín],
Fusion of Gaussian Kernels Within Support Vector Classification,
CIARP06(945-953).
Springer DOI 0611
BibRef

Lin, H.J.[Hwei-Jen], Yeh, J.P.[Jih Pin],
A hybrid optimization strategy for simplifying the solutions of support vector machines,
PRL(31), No. 7, 1 May 2010, pp. 563-571.
Elsevier DOI 1004
Support vector machine; Particle swarm optimization; Genetic algorithm; Optimization; Discriminant function; Hyperplane BibRef

Wang, X.M.[Xiao-Ming], Chung, F.L.[Fu-Lai], Wang, S.T.[Shi-Tong],
On minimum class locality preserving variance support vector machine,
PR(43), No. 8, August 2010, pp. 2753-2762.
Elsevier DOI 1006
Supervised learning; Support vector machine; Minimum class variance support machine; Locality preserving projections BibRef

Glasmachers, T.[Tobias], Igel, C.[Christian],
Maximum Likelihood Model Selection for 1-Norm Soft Margin SVMs with Multiple Parameters,
PAMI(32), No. 8, August 2010, pp. 1522-1528.
IEEE DOI 1007
With few initial points. BibRef

Cevikalp, H.[Hakan],
New clustering algorithms for the support vector machine based hierarchical classification,
PRL(31), No. 11, 1 August 2010, pp. 1285-1291.
Elsevier DOI 1008
Hierarchical classification; Support vector machines; Multi-class classification; Clustering; Normalized cuts BibRef

Saha, S.K.[Sujan Kumar], Narayan, S.[Shashi], Sarkar, S.[Sudeshna], Mitra, P.[Pabitra],
A composite kernel for named entity recognition,
PRL(31), No. 12, 1 September 2010, pp. 1591-1597.
Elsevier DOI 1008
Named entity recognition; Support vector machine; Kernel methods; String kernel; Machine learning BibRef

Kumar, M.A.[M. Arun], Gopal, M.,
A hybrid SVM based decision tree,
PR(43), No. 12, December 2010, pp. 3977-3987.
Elsevier DOI 1003
Support vector machines; Decision trees; Hybridization; Pattern recognition BibRef

Giacco, F., Thiel, C., Pugliese, L., Scarpetta, S., Marinaro, M.,
Uncertainty Analysis for the Classification of Multispectral Satellite Images Using SVMs and SOMs,
GeoRS(48), No. 10, October 2010, pp. 3769-3779.
IEEE DOI 1003
BibRef

Huang, K.Z.[Kai-Zhu], Zheng, D.N.[Da-Nian], Sun, J.[Jun], Hotta, Y.[Yoshinobu], Fujimoto, K.[Katsuhito], Naoi, S.[Satoshi],
Sparse learning for support vector classification,
PRL(31), No. 13, 1 October 2010, pp. 1944-1951.
Elsevier DOI 1003
Sparse representation; Implementations of L0-norm; Regularization term; Support vector machine; Kernel methods BibRef

Ye, Q.[Qiaolin], Zhao, C.X.[Chun-Xia], Ye, N.[Ning], Chen, Y.N.[Yan-Nan],
Multi-weight vector projection support vector machines,
PRL(31), No. 13, 1 October 2010, pp. 2006-2011.
Elsevier DOI 1003
Generalized eigenvalues; Multi-weight vector; Matrix singularity; Standard eigenvalues; Singular problems BibRef

Ye, Q.[Qiaolin], Ye, N.[Ning], Yin, T.M.[Tong-Ming],
Enhanced multi-weight vector projection support vector machine,
PRL(42), No. 1, 2014, pp. 91-100.
Elsevier DOI 1404
Multiple weight vectors BibRef

Bovolo, F., Bruzzone, L., Carlin, L.,
A Novel Technique for Subpixel Image Classification Based on Support Vector Machine,
IP(19), No. 11, November 2010, pp. 2983-2999.
IEEE DOI 1011
BibRef

Ertekin, S.[Seyda], Bottou, L.[Leon], Giles, C.L.[C. Lee],
Nonconvex Online Support Vector Machines,
PAMI(33), No. 2, February 2011, pp. 368-381.
IEEE DOI 1101
Ramp Loss. Supress influence of outliesrs. BibRef

Han, D.Q.[De-Qiang], Han, C.Z.[Chong-Zhao], Yang, Y.[Yi],
A novel classifier based on shortest feature line segment,
PRL(32), No. 3, 1 February 2011, pp. 485-493.
Elsevier DOI 1101
Nearest feature line (NFL); Trespass inaccuracy; Feature line segment; Geometric relation; Neighborhood-based classifier BibRef

Han, D.Q.[De-Qiang], Han, C.Z.[Chong-Zhao], Yang, Y.[Yi], Liu, Y.[Yu], Mao, W.T.[Wen-Tao],
Pre-extracting method for SVM classification based on the non-parametric K-NN rule,
ICPR08(1-4).
IEEE DOI 0812
BibRef

Chang, C.C.[Chih-Cheng], Chien, L.J.[Li-Jen], Lee, Y.J.[Yuh-Jye],
A novel framework for multi-class classification via ternary smooth support vector machine,
PR(44), No. 6, June 2011, pp. 1235-1244.
Elsevier DOI 1102
Confidence; Hidden classes; Multi-class classification; Smooth method; Support vector machine; Ternary voting games BibRef

Guo, L.H.[Li-Hua], Jin, L.W.[Lian-Wen],
Laplacian Support Vector Machines with Multi-Kernel Learning,
IEICE(E94-D), No. 2, February 2011, pp. 379-383.
WWW Link. 1102
BibRef

Sahbi, H.[Hichem], Audibert, J.Y.[Jean-Yves], Keriven, R.[Renaud],
Context-Dependent Kernels for Object Classification,
PAMI(33), No. 4, April 2011, pp. 699-708.
IEEE DOI 1103
Not just correlation kernels. BibRef

Sahbi, H.[Hichem], Audibert, J.Y.[Jean-Yves], Rabarisoa, J.[Jaonary], Keriven, R.[Renaud],
Context-dependent kernel design for object matching and recognition,
CVPR08(1-8).
IEEE DOI 0806
BibRef

Sahbi, H.[Hichem], Fleuret, F.[François],
Scale-Invariance of Support Vector Machines based on the Triangular Kernel,
INRIARR-4601, Octobre 2002.
HTML Version. 0306
BibRef

Sahbi, H.[Hichem], Li, X.[Xi],
Context-Based Support Vector Machines for Interconnected Image Annotation,
ACCV10(I: 214-227).
Springer DOI 1011
Award, ACCV. BibRef

Jiu, M., Sahbi, H.[Hichem],
Nonlinear Deep Kernel Learning for Image Annotation,
IP(26), No. 4, April 2017, pp. 1820-1832.
IEEE DOI 1704
Feature extraction BibRef

Veenman, C.J.[Cor J.], Bolck, A.[Annabel],
A sparse nearest mean classifier for high dimensional multi-class problems,
PRL(32), No. 6, 15 April 2011, pp. 854-859.
Elsevier DOI 1103
Classification; Multi-class; Support vector machine; High dimensional; Chemometrics; Bioinformatics BibRef

Ozer, S.[Sedat], Chen, C.H.[Chi H.], Cirpan, H.A.[Hakan A.],
A set of new Chebyshev kernel functions for support vector machine pattern classification,
PR(44), No. 7, July 2011, pp. 1435-1447.
Elsevier DOI 1103
Generalized Chebyshev kernel; Modified Chebyshev kernel; Semi-parametric kernel; Kernel construction BibRef

Ozer, S.[Sedat], Chen, C.H.[Chi Hau],
Generalized Chebyshev Kernels for Support Vector Classification,
ICPR08(1-4).
IEEE DOI 0812
BibRef

Ozer, S.[Sedat],
Similarity Domains Machine for Scale-Invariant and Sparse Shape Modeling,
IP(28), No. 2, February 2019, pp. 534-545.
IEEE DOI 1811
image classification, image filtering, image representation, support vector machines, similarity domains machine, sparse spatial kernel machine BibRef

Wang, Z.[Zheng], Yan, S.C.[Shui-Cheng], Zhang, C.S.[Chang-Shui],
Active learning with adaptive regularization,
PR(44), No. 10-11, October-November 2011, pp. 2375-2383.
Elsevier DOI 1101
Active learning; Adaptive regularization; SVM; TSVM BibRef

Peng, X.J.[Xin-Jun],
TPMSVM: A novel twin parametric-margin support vector machine for pattern recognition,
PR(44), No. 10-11, October-November 2011, pp. 2678-2692.
Elsevier DOI 1101
Support vector machine; Twin support vector machine; Nonparallel hyperplanes; Heteroscedastic noise structure; Parametric-margin model BibRef

Chen, X.B.[Xiao-Bo], Yang, J.[Jian], Ye, Q.L.[Qiao-Lin], Liang, J.[Jun],
Recursive projection twin support vector machine via within-class variance minimization,
PR(44), No. 10-11, October-November 2011, pp. 2643-2655.
Elsevier DOI 1101
Multiple-surface classifier; Twin support vector machine; Quadratic programming BibRef

Wittek, P.[Peter], Tan, C.L.[Chew Lim],
Compactly Supported Basis Functions as Support Vector Kernels for Classification,
PAMI(33), No. 10, October 2011, pp. 2039-2050.
IEEE DOI 1109
Wavelet kernels. Use inner product of kernels. BibRef

Laanaya, H.[Hicham], Abdallah, F.[Fahed], Snoussi, H.[Hichem], Richard, C.[Cédric],
Learning general Gaussian kernel hyperparameters of SVMs using optimization on symmetric positive-definite matrices manifold,
PRL(32), No. 13, 1 October 2011, pp. 1511-1515.
Elsevier DOI 1109
Kernel optimization; Support vector machines; General Gaussian kernel; Symmetric positive-definite matrices manifold BibRef

Li, B.[Bing], Song, S.J.[Shi-Ji], Li, K.[Kang],
Improved conjugate gradient implementation for least squares support vector machines,
PRL(33), No. 2, 15 January 2012, pp. 121-125.
Elsevier DOI 1112
Least square; Support vector machine; Unconstrained optimization; Conjugate gradient method BibRef

Clark, A.R.J.[Andrew R.J.], Everson, R.M.[Richard M.],
Multi-objective learning of Relevance Vector Machine classifiers with multi-resolution kernels,
PR(45), No. 9, September 2012, pp. 3535-3543.
Elsevier DOI 1206
Relevance Vector Machine; Evolutionary algorithm; Classification; Multi-resolution kernels; Cross-validation BibRef

Gkalelis, N., Mezaris, V., Kompatsiaris, I., Stathaki, T.,
Linear Subclass Support Vector Machines,
SPLetters(19), No. 9, September 2012, pp. 575-578.
IEEE DOI 1208
BibRef

Wu, J.X.[Jian-Xin],
Efficient HIK SVM Learning for Image Classification,
IP(21), No. 10, October 2012, pp. 4442-4453.
IEEE DOI 1209
BibRef
Earlier:
Power mean SVM for large scale visual classification,
CVPR12(2344-2351).
IEEE DOI 1208
BibRef
Earlier:
A Fast Dual Method for HIK SVM Learning,
ECCV10(II: 552-565).
Springer DOI 1009
BibRef

Wang, C.D.[Chang-Dong], Lai, J.H.[Jian-Huang],
Position regularized Support Vector Domain Description,
PR(46), No. 3, March 2013, pp. 875-884.
Elsevier DOI 1212
Support Vector Domain Description; Weighting; Data clustering; Support vector clustering; SVDD k-Means BibRef

Gonzalez-Abril, L., Velasco, F., Angulo, C., Ortega, J.A.,
A study on output normalization in multiclass SVMs,
PRL(34), No. 3, 1 February 2013, pp. 344-348.
Elsevier DOI 1301
1-v-r SVM; Convex hull; Kernel methods; Multiclassification BibRef

Minoura, K.[Kentaro], Tamura, S.[Satoshi], Hayamizu, S.[Satoru],
Probabilistic expression of Polynomial Semantic Indexing and its application for classification,
PRL(34), No. 13, 2013, pp. 1485-1489.
Elsevier DOI 1308
Polynomial Semantic Indexing BibRef

Abrahamsen, T.J.[Trine Julie], Hansen, L.K.[Lars Kai],
Variance inflation in high dimensional Support Vector Machines,
PRL(34), No. 16, 2013, pp. 2173-2180.
Elsevier DOI 1310
Variance inflation BibRef

Faußer, S.[Stefan], Schwenker, F.[Friedhelm],
Semi-supervised clustering of large data sets with kernel methods,
PRL(37), No. 1, 2014, pp. 78-84.
Elsevier DOI 1402
BibRef
Earlier:
Clustering large datasets with kernel methods,
ICPR12(501-504).
WWW Link. 1302
Semi-supervised clustering BibRef

Cheng, Q.A.[Qi-Ang], Tezcan, J.[Jale], Cheng, J.[Jie],
Confidence and prediction intervals for semiparametric mixed-effect least squares support vector machine,
PRL(40), No. 1, 2014, pp. 88-95.
Elsevier DOI 1403
Semiparametric function estimation BibRef

Demir, B.[Begüm], Bruzzone, L.[Lorenzo],
A multiple criteria active learning method for support vector regression,
PR(47), No. 7, 2014, pp. 2558-2567.
Elsevier DOI 1404
Regression BibRef

Demir, B.[Begm], Ertrk, S.[Sarp],
Improving SVM classification accuracy using a hierarchical approach for hyperspectral images,
ICIP09(2849-2852).
IEEE DOI 0911
BibRef

Serra-Toro, C.[Carlos], Traver, V.J.[V. Javier], Pla, F.[Filiberto],
Exploring some practical issues of SVM+: Is really privileged information that helps?,
PRL(42), No. 1, 2014, pp. 40-46.
Elsevier DOI 1404
Privileged information BibRef

Chen, W.J.[Wei-Jie], Shao, Y.H.[Yuan-Hai], Li, C.N.[Chun-Na], Deng, N.Y.[Nai-Yang],
MLTSVM: A novel twin support vector machine to multi-label learning,
PR(52), No. 1, 2016, pp. 61-74.
Elsevier DOI 1601
Multi-label classification BibRef

Zhang, H.X.[Hua-Xiang], Cao, L.L.[Lin-Lin], Gao, S.[Shuang],
A locality correlation preserving support vector machine,
PR(47), No. 9, 2014, pp. 3168-3178.
Elsevier DOI 1406
Support vector machine BibRef

Tuia, D., Volpi, M., Dalla Mura, M., Rakotomamonjy, A., Flamary, R.,
Automatic Feature Learning for Spatio-Spectral Image Classification With Sparse SVM,
GeoRS(52), No. 10, October 2014, pp. 6062-6074.
IEEE DOI 1407
Feature extraction BibRef

Nasiri, J.A.[Jalal A.], Charkari, N.M.[Nasrollah Moghadam], Jalili, S.[Saeed],
Least squares twin multi-class classification support vector machine,
PR(48), No. 3, 2015, pp. 984-992.
Elsevier DOI 1412
Twin support vector machine BibRef

Peng, S.L.[Shi-Li], Hu, Q.H.[Qing-Hua], Chen, Y.L.[Yin-Li], Dang, J.[Jianwu],
Improved support vector machine algorithm for heterogeneous data,
PR(48), No. 6, 2015, pp. 2072-2083.
Elsevier DOI 1503
Support vector machine BibRef

Chen, J.H.[Jin-Hui], Takiguchi, T.[Tetsuya], Ariki, Y.[Yasuo],
A robust SVM classification framework using PSM for multi-class recognition,
JIVP(2015), No. 1, 2015, pp. 7.
DOI Link 1503
BibRef
And:
Multithreading AdaBoost framework for object recognition,
ICIP15(1235-1239)
IEEE DOI 1512
AUC; Ri-HOG; multithreading AdaBoost BibRef

Chen, J.H.[Jin-Hui], Kitano, Y.[Yosuke], Li, Y.T.[Yi-Ting], Takiguchi, T.[Tetsuya], Ariki, Y.[Yasuo],
A Robust Learning Framework Using PSM and Ameliorated SVMs for Emotional Recognition,
CV4AC14(629-643).
Springer DOI 1504
BibRef

Xu, J., Tang, Y.Y.[Yuan Yan], Zou, B.[Bin], Xu, Z.B.[Zong-Ben], Li, L.Q.[Luo-Qing], Lu, Y.[Yang], Zhang, B.,
The Generalization Ability of SVM Classification Based on Markov Sampling,
Cyber(45), No. 6, June 2015, pp. 1169-1179.
IEEE DOI 1506
Cybernetics BibRef

Li, Y.[Ya], Tian, X.M.[Xin-Mei], Song, M.L.[Ming-Li], Tao, D.C.[Da-Cheng],
Multi-task proximal support vector machine,
PR(48), No. 10, 2015, pp. 3249-3257.
Elsevier DOI 1507
Multi-task learning BibRef

Bae, J.S.[Ji-Sang], Kim, J.O.[Jong-Ok],
Multiclass Probabilistic Classification for Support Vector Machines,
IEICE(E98-D), No. 6, June 2015, pp. 1251-1255.
WWW Link. 1505
BibRef

Zheng, S.F.[Song-Feng],
Smoothly approximated support vector domain description,
PR(49), No. 1, 2016, pp. 55-64.
Elsevier DOI 1511
Support vector domain description BibRef

Ferreira, M.R.P.[Marcelo R.P.], de Carvalho, F.A.T.[Francisco A.T.], Simões, E.C.[Eduardo C.],
Kernel-based hard clustering methods with kernelization of the metric and automatic weighting of the variables,
PR(51), No. 1, 2016, pp. 310-321.
Elsevier DOI 1601
Kernel clustering BibRef

de Carvalho, F.A.T.[Francisco A.T.], Simões, E.C.[Eduardo C.], Santana, L.V.C.[Lucas V.C.], Ferreira, M.R.P.[Marcelo R.P.],
Gaussian kernel c-means hard clustering algorithms with automated computation of the width hyper-parameters,
PR(79), 2018, pp. 370-386.
Elsevier DOI 1804
Gaussian kernel clustering, Kernelization of the metric, Feature space, Width hyper-parameter BibRef

Simões, E.C.[Eduardo C.], de Carvalho, F.A.T.[Francisco A. T.],
Gaussian kernel fuzzy c-means with width parameter computation and regularization,
PR(143), 2023, pp. 109749.
Elsevier DOI 2310
Gaussian kernel fuzzy clustering, Kernelization of the metric, Width parameter, Entropy regularization BibRef

Osadchy, M.[Margarita], Keren, D.[Daniel], Raviv, D.,
Recognition Using Hybrid Classifiers,
PAMI(38), No. 4, April 2016, pp. 759-771.
IEEE DOI 1603
Computational complexity BibRef

Osadchy, M.[Margarita], Keren, D.[Daniel], Fadida-Specktor, B.[Bella],
Hybrid Classifiers for Object Classification with a Rich Background,
ECCV12(V: 284-297).
Springer DOI 1210
treat the non-object as background. BibRef

Osadchy, M.[Margarita], Keren, D.[Daniel],
Incorporating the Boltzmann Prior in Object Detection Using SVM,
CVPR06(II: 2095-2101).
IEEE DOI 0606
BibRef

Samat, A.[Alim], Gamba, P.[Paolo], Abuduwaili, J.[Jilili], Liu, S.C.[Si-Cong], Miao, Z.[Zelang],
Geodesic Flow Kernel Support Vector Machine for Hyperspectral Image Classification by Unsupervised Subspace Feature Transfer,
RS(8), No. 3, 2016, pp. 234.
DOI Link 1604
BibRef

Moghaddam, V.H.[Vahid Hooshmand], Hamidzadeh, J.[Javad],
New Hermite orthogonal polynomial kernel and combined kernels in Support Vector Machine classifier,
PR(60), No. 1, 2016, pp. 921-935.
Elsevier DOI 1609
Support Vector Machine (SVM) BibRef

Ji, Y.S.[Ying-Sheng], Chen, Y.S.[Yu-Shu], Fu, H.H.[Hao-Huan], Yang, G.W.[Guang-Wen],
An EnKF-based scheme to optimize hyper-parameters and features for SVM classifier,
PR(62), No. 1, 2017, pp. 202-213.
Elsevier DOI 1705
EnKF BibRef

Xu, G.[Guibiao], Cao, Z.[Zheng], Hu, B.G.[Bao-Gang], Principe, J.C.[Jose C.],
Robust support vector machines based on the rescaled hinge loss function,
PR(63), No. 1, 2017, pp. 139-148.
Elsevier DOI 1612
Support vector machine BibRef

Cheng, F.Y.[Fan-Yong], Zhang, J.[Jing], Li, Z.Y.[Zuo-Yong], Tang, M.Z.[Ming-Zhu],
Double distribution support vector machine,
PRL(88), No. 1, 2017, pp. 20-25.
Elsevier DOI 1703
Minimum margin BibRef

Lin, L.[Liang], Wang, G.R.[Guang-Run], Zuo, W.M.[Wang-Meng], Feng, X.C.[Xiang-Chu], Zhang, L.[Lei],
Cross-Domain Visual Matching via Generalized Similarity Measure and Feature Learning,
PAMI(39), No. 6, June 2017, pp. 1089-1102.
IEEE DOI 1705
Euclidean distance, Face, Neural networks, Pattern matching, Videos, Visualization, Similarity model, cross-domain matching, deep learning, person, verification BibRef

Wang, S.S.[Shan-Shan], Zhang, L.[Lei], Zuo, W.M.[Wang-Meng], Zhang, B.,
Class-Specific Reconstruction Transfer Learning for Visual Recognition Across Domains,
IP(29), 2020, pp. 2424-2438.
IEEE DOI 2001
BibRef
Earlier: A1, A2, A3, Only:
Class-Specific Reconstruction Transfer Learning via Sparse Low-Rank Constraint,
CEFR-LCV17(949-957)
IEEE DOI 1802
Image reconstruction, Adaptation models, Machine learning, Data models, Correlation, Semantics, Learning systems, image classification BibRef

Gu, B.[Bin], Sheng, V.S.[Victor S.], Tay, K.Y.[Keng Yeow], Romano, W.[Walter], Li, S.[Shuo],
Cross Validation Through Two-Dimensional Solution Surface for Cost-Sensitive SVM,
PAMI(39), No. 6, June 2017, pp. 1103-1121.
IEEE DOI 1705
Computational modeling, Fasteners, Kernel, Search methods, Space exploration, Support vector machines, Training, Solution surface, cost-sensitive support vector machine, cross validation, solution path, space, partition BibRef

Zhang, X.[Xin], Zhang, X.[Xiu],
Withdrawn: Adaptive multiclass support vector machine for multimodal data analysis,
PR(70), No. 1, 2017, pp. 177-184.
Elsevier DOI 1706
BibRef
And: Withdrawn - premature publication. PR(76), No. 1, 2018, pp. 762.
Elsevier DOI 1801
Artificial bee colony BibRef

Maggu, J.[Jyoti], Majumdar, A.[Angshul],
Kernel transform learning,
PRL(98), No. 1, 2017, pp. 117-122.
Elsevier DOI 1710
BibRef
And:
Greedy deep transform learning,
ICIP17(1822-1826)
IEEE DOI 1803
Dictionaries, Inverse problems, Machine learning, Neural networks, Tools, Training, Transforms, deep learning, greedy learning, transform learning BibRef

Yan, H.[He], Ye, Q.L.[Qiao-Lin], Zhang, T.A.[Tian-An], Yu, D.J.[Dong-Jun], Yuan, X.[Xia], Xu, Y.Q.[Yi-Qing], Fu, L.Y.[Li-Yong],
Least squares twin bounded support vector machines based on L1-norm distance metric for classification,
PR(74), No. 1, 2018, pp. 434-447.
Elsevier DOI 1711
L1-LSTBSVM BibRef

Dai, J.S.[Ji-Sheng], Xu, W.C.[Wei-Chao], Ye, Z.F.[Zhong-Fu], Chang, C.Q.[Chun-Qi],
Effective subset approach for SVMpath singularities,
PRL(100), No. 1, 2017, pp. 51-58.
Elsevier DOI 1712
Support vector machine (SVM) BibRef

Zheng, Q.Q.[Qing-Qing], Zhu, F.Y.[Feng-Yuan], Qin, J.[Jing], Chen, B.[Badong], Heng, P.A.[Pheng-Ann],
Sparse Support Matrix Machine,
PR(76), No. 1, 2018, pp. 715-726.
Elsevier DOI 1801
Classification BibRef

Chen, G.[Gang], Xu, R.[Ran], Yang, Z.[Zhi],
Deep ranking structural support vector machine for image tagging,
PRL(105), 2018, pp. 30-38.
Elsevier DOI 1804
Image tagging, Maximum margin learning, Deep learning, Ranking BibRef

Gu, B.[Bin], Quan, X.[Xin], Gu, Y.H.[Yun-Hua], Sheng, V.S.[Victor S.], Zheng, G.S.[Guan-Sheng],
Chunk incremental learning for cost-sensitive hinge loss support vector machine,
PR(83), 2018, pp. 196-208.
Elsevier DOI 1808
Cost-sensitive learning, Chunk incremental learning, Hinge loss, Support vector machines BibRef

Chen, H.Y.[Hai-Yan], Yu, Y.[Ying], Jia, Y.Z.[Yi-Zhen], Gu, B.[Bin],
Incremental learning for transductive support vector machine,
PR(133), 2023, pp. 108982.
Elsevier DOI 2210
Transductive support vector machine, Incremental learning, Non-convex optimization, Infinitesimal annealing BibRef

Padierna, L.C.[Luis Carlos], Carpio, M.[Martín], Rojas-Domínguez, A.[Alfonso], Puga, H.[Héctor], Fraire, H.[Héctor],
A novel formulation of orthogonal polynomial kernel functions for SVM classifiers: The Gegenbauer family,
PR(84), 2018, pp. 211-225.
Elsevier DOI 1809
SVM classifier, Orthogonal polynomials, Gegenbauer kernel, Binary classification BibRef

Tzelepis, C.[Christos], Mezaris, V.[Vasileios], Patras, I.[Ioannis],
Linear Maximum Margin Classifier for Learning from Uncertain Data,
PAMI(40), No. 12, December 2018, pp. 2948-2962.
IEEE DOI 1811
Uncertainty, Support vector machines, Gaussian processes, Brain modeling, Statistical analysis, Covariance matrices, statistical learning theory BibRef

Liu, W., Shen, X., Du, B., Tsang, I.W., Zhang, W., Lin, X.,
Hyperspectral Imagery Classification via Stochastic HHSVMs,
IP(28), No. 2, February 2019, pp. 577-588.
IEEE DOI 1811
computational complexity, geophysical image processing, hyperspectral imaging, image classification, iterative methods, HHSVM BibRef

Abe, S.[Shigeo],
Are twin hyperplanes necessary?,
PRL(116), 2018, pp. 218-224.
Elsevier DOI 1812
BibRef

Ma, J.J.[Jia-Jun], Zhou, S.[Shuisheng], Chen, L.[Li], Wang, W.W.[Wei-Wei], Zhang, Z.[Zhuan],
A sparse robust model for large scale multi-class classification based on K-SVCR,
PRL(117), 2019, pp. 16-23.
Elsevier DOI 1901
SVM, Multi-class classification, Outliers, K-SVCR, Sparse solution BibRef

Weerasinghe, S.[Sandamal], Erfani, S.M.[Sarah M.], Alpcan, T.[Tansu], Leckie, C.[Christopher],
Support vector machines resilient against training data integrity attacks,
PR(96), 2019, pp. 106985.
Elsevier DOI 1909
Support Vector Machines, Integrity attack BibRef

Menezes, M.V.F.[Murilo V.F.], Torres, L.C.B.[Luiz C.B.], Braga, A.P.[Antonio P.],
Width optimization of RBF kernels for binary classification of support vector machines: A density estimation-based approach,
PRL(128), 2019, pp. 1-7.
Elsevier DOI 1912
Classification, RBF Kernel, Support vector machine, Density estimation BibRef

Alam, S.[Shamshe], Sonbhadra, S.K.[Sanjay Kumar], Agarwal, S.[Sonali], Nagabhushan, P., Tanveer, M.,
Sample reduction using farthest boundary point estimation (FBPE) for support vector data description (SVDD),
PRL(131), 2020, pp. 268-276.
Elsevier DOI 2004
Support vector data description (SVDD), Data distribution, Sample reduction, Farthest boundary point BibRef

Okwuashi, O.[Onuwa], Ndehedehe, C.E.[Christopher E.],
Deep support vector machine for hyperspectral image classification,
PR(103), 2020, pp. 107298.
Elsevier DOI 2005
Remote sensing, Hyperspectral image, Deep support vector machine, Image classification BibRef

Gu, B.[Bin], Geng, X.[Xiang], Shi, W.L.[Wan-Li], Shan, Y.Y.[Ying-Ying], Huang, Y.F.[Yu-Fang], Wang, Z.J.[Zhi-Jie], Zheng, G.S.[Guan-Sheng],
Solving large-scale support vector ordinal regression with asynchronous parallel coordinate descent algorithms,
PR(109), 2021, pp. 107592.
Elsevier DOI 2009
Asynchronous parallel, Coordinate descent, Support vector, Ordinal regression BibRef

Sohrab, F.[Fahad], Raitoharju, J.[Jenni], Iosifidis, A.[Alexandros], Gabbouj, M.[Moncef],
Multimodal subspace support vector data description,
PR(110), 2021, pp. 107648.
Elsevier DOI 2011
BibRef
Earlier: A1, A2, A4, A3:
Subspace Support Vector Data Description,
ICPR18(722-727)
IEEE DOI 1812
Feature transformation, Multimodal data, One-class classification, Support vector data description, Subspace learning. Optimization, Training, Kernel, Data models, Support vector machine classification BibRef

Sohrab, F.[Fahad], Iosifidis, A.[Alexandros], Gabbouj, M.[Moncef], Raitoharju, J.[Jenni],
Graph-embedded subspace support vector data description,
PR(133), 2023, pp. 108999.
Elsevier DOI 2210
One-Class classification, Support vector data description, Subspace learning, Spectral regression BibRef

Chaudhuri, A.[Arin], Sadek, C.[Carol], Kakde, D.[Deovrat], Wang, H.Y.[Hao-Yu], Hu, W.H.[Wen-Hao], Jiang, H.[Hansi], Kong, S.H.[Seung-Hyun], Liao, Y.W.[Yu-Wei], Peredriy, S.[Sergiy],
The trace kernel bandwidth criterion for support vector data description,
PR(111), 2021, pp. 107662.
Elsevier DOI 2012
Support vector data description, SVDD, One-class support vector machines, OCSVM, Gaussian kernel, Gaussian kernel bandwidth BibRef

Liu, X., Guan, Y.L., Xu, Q.,
Support Vector Machine-Based Blind Equalization for High-Order QAM With Short Data Length,
SPLetters(28), 2021, pp. 259-263.
IEEE DOI 2102
Quadrature amplitude modulation, Blind equalizers, Convergence, Reactive power, Cost function, Support vector machines, support vector regression BibRef

Lantzanakis, G.[Giannis], Mitraka, Z.[Zina], Chrysoulakis, N.[Nektarios],
X-SVM: An Extension of C-SVM Algorithm for Classification of High-Resolution Satellite Imagery,
GeoRS(59), No. 5, May 2021, pp. 3805-3815.
IEEE DOI 2104
Support vector machines, Training data, Static VAr compensators, Training, Remote sensing, Earth, Satellites, urban areas BibRef

An, Y.X.[Yue-Xuan], Xue, H.[Hui],
Indefinite twin support vector machine with DC functions programming,
PR(121), 2022, pp. 108195.
Elsevier DOI 2109
SVM, TWSVM, Indefinite kernel, DC Programming, Structural risk minimization principle BibRef

Chen, C.[Cong], Batselier, K.[Kim], Yu, W.J.[Wen-Jian], Wong, N.[Ngai],
Kernelized support tensor train machines,
PR(122), 2022, pp. 108337.
Elsevier DOI 2112
Image classification, Tensor, Support tensor machine BibRef

Singh, D.[Dalwinder], Singh, B.[Birmohan],
Feature wise normalization: An effective way of normalizing data,
PR(122), 2022, pp. 108307.
Elsevier DOI 2112
Data normalization, -nearest neighbor classification, Machine learning, Metaheuristic optimization, Support vector machines BibRef

Wang, H.Q.[Hai-Qi], Li, L.K.[Liu-Ke], Che, L.[Lei], Kong, H.R.[Hao-Ran], Wang, Q.[Qiong], Wang, Z.H.[Zhi-Hai], Xu, J.B.[Jian-Bo],
Geospatial Least Squares Support Vector Regression Fused with Spatial Weight Matrix,
IJGI(10), No. 11, 2021, pp. xx-yy.
DOI Link 2112
BibRef

Shu, Y.Y.[Yang-Yang], Li, Q.[Qian], Xu, C.[Chang], Liu, S.W.[Shao-Wu], Xu, G.D.[Guan-Dong],
V-SVR+: Support Vector Regression With Variational Privileged Information,
MultMed(24), 2022, pp. 876-889.
IEEE DOI 2202
Training, Support vector machines, Task analysis, Testing, Optimization, Object recognition, Kernel, variational privileged information BibRef

Maldonado, S.[Sebastián], López, J.[Julio], Carrasco, M.[Miguel],
The Cobb-Douglas Learning Machine,
PR(128), 2022, pp. 108701.
Elsevier DOI 2205
Cobb-Douglas, Minimax Probability Machine, Minimum Error Minimax Probability Machine, Support Vector Machines BibRef

Liu, G.X.[Guang-Xin], Wang, L.G.[Li-Guo], Liu, D.F.[Dan-Feng], Fei, L.[Lei], Yang, J.H.[Jing-Hui],
Hyperspectral Image Classification Based on Non-Parallel Support Vector Machine,
RS(14), No. 10, 2022, pp. xx-yy.
DOI Link 2206
BibRef

Liu, G.X.[Guang-Xin], Wang, L.G.[Li-Guo], Liu, D.F.[Dan-Feng],
Hyperspectral Image Classification Based on a Least Square Bias Constraint Additional Empirical Risk Minimization Nonparallel Support Vector Machine,
RS(14), No. 17, 2022, pp. xx-yy.
DOI Link 2209
BibRef

Akram-Ali-Hammouri, Z.[Ziad], Fernández-Delgado, M.[Manuel], Cernadas, E.[Eva], Barro, S.[Senén],
Fast Support Vector Classification for Large-Scale Problems,
PAMI(44), No. 10, October 2022, pp. 6184-6195.
IEEE DOI 2209
Training, Support vector machines, Static VAr compensators, Memory management, Prototypes, Kernel, Random access memory, model selection BibRef

Guo, Y.[Yiwen], Zhang, C.S.[Chang-Shui],
Recent Advances in Large Margin Learning,
PAMI(44), No. 10, October 2022, pp. 7167-7174.
IEEE DOI 2209
Robustness, Training, Perturbation methods, Neural networks, Support vector machines, Stochastic processes, Kernel, deep neural networks BibRef

Wang, H.J.[Hua-Jun], Shao, Y.H.[Yuan-Hai], Zhou, S.L.[Sheng-Long], Zhang, C.[Ce], Xiu, N.[Naihua],
Support Vector Machine Classifier via L_0/1 Soft-Margin Loss,
PAMI(44), No. 10, October 2022, pp. 7253-7265.
IEEE DOI 2209
Support vector machines, Training, Optimization, Fasteners, Training data, Standards, Robustness, L0/1 soft-margin loss, L_0/1 ADMM BibRef

Marchetti, F., Perracchione, E.,
Local-to-Global Support Vector Machines (LGSVMs),
PR(132), 2022, pp. 108920.
Elsevier DOI 2209
Local-to-global support vector machines, Partition of unity, Supervised classification, Kernel models BibRef

Ramezan, C.A.[Christopher A.],
Transferability of Recursive Feature Elimination (RFE)-Derived Feature Sets for Support Vector Machine Land Cover Classification,
RS(14), No. 24, 2022, pp. xx-yy.
DOI Link 2212
BibRef

Zhang, R.[Rui], Jiao, Z.H.[Zi-Heng], Zhang, H.Y.[Hong-Yuan], Li, X.L.[Xue-Long],
Manifold Neural Network With Non-Gradient Optimization,
PAMI(45), No. 3, March 2023, pp. 3986-3993.
IEEE DOI 2302
Manifolds, Support vector machines, Convergence, Biological neural networks, Optimization, Feature extraction, non-gradient optimization BibRef

Wan, Y.L.[Yi-Liang], Fei, Y.[Yuwen], Jin, R.[Rui], Wu, T.[Tao], He, X.[Xinguang],
An Object-Oriented Deep Multi-Sphere Support Vector Data Description Method for Impervious Surfaces Extraction Based on Multi-Sourced Data,
IJGI(12), No. 6, 2023, pp. xx-yy.
DOI Link 2307
BibRef

Rezvani, S.[Salim], Wu, J.H.[Jun-Hao],
Handling Multi-Class Problem by Intuitionistic Fuzzy Twin Support Vector Machines Based on Relative Density Information,
PAMI(45), No. 12, December 2023, pp. 14653-14664.
IEEE DOI 2311
BibRef

Liang, R.M.[Rong-Mei], Wu, X.F.[Xiao-Fei], Zhang, Z.M.[Zhi-Min],
Linearized alternating direction method of multipliers for elastic-net support vector machines,
PR(148), 2024, pp. 110134.
Elsevier DOI 2402
Convex optimization, Linearized ADMM, Elastic-net, Support vector machines BibRef

Tran, N.K.[Nguyen Khoa], Kühle, L.C.[Laura C.], Klau, G.W.[Gunnar W.],
A critical review of multi-output support vector regression,
PRL(178), 2024, pp. 69-75.
Elsevier DOI 2402
Multi-output regression, Support vector, Least-squares BibRef


Nalepa, J.[Jakub], Dudzik, W.[Wojciech], Kawulok, M.[Michal],
Memetic Evolution of Training Sets with Adaptive Radial Basis Kernels for Support Vector Machines,
ICPR21(5503-5510)
IEEE DOI 2105
Training, Support vector machines, Memetics, Supervised learning, Training data, Big Data applications, Pattern recognition BibRef

Schleif, F.M.[Frank-Michael], Raab, C.[Christoph], Tino, P.[Peter],
Sparsification of Indefinite Learning Models,
SSSPR18(173-183).
Springer DOI 1810
BibRef

Mygdalis, V., Tefas, A., Pitas, I.,
Learning Multi-Graph Regularization for SVM Classification,
ICIP18(1608-1612)
IEEE DOI 1809
Support vector machines, Kernel, Optimization, Training data, Standards, Face recognition, Semantics, object recognition BibRef

Lyu, X.R.[Xin-Rui], Zepeda, J.[Joaquin], Perez, P.[Patrick],
Maximum Margin Linear Classifiers in Unions of Subspaces,
BMVC16(xx-yy).
HTML Version. 1805
Union-of-Subspaces SVM. BibRef

Blaschko, M.B.[Matthew B.],
Slack and Margin Rescaling as Convex Extensions of Supermodular Functions,
EMMCVPR17(439-454).
Springer DOI 1805
variants of the structured output SVM. BibRef

Tran, D.T., Waris, M.A., Gabbouj, M.[Moncef], Iosifidis, A.[Alexandros],
Sample-based regularization for support vector machine classification,
IPTA17(1-6)
IEEE DOI 1804
image classification, support vector machines, SVM, human action recognition tasks, linear combination, kernel methods BibRef

Huang, D., Wang, C.D., Lai, J.H., Liang, Y., Bian, S., Chen, Y.,
Ensemble-driven support vector clustering: From ensemble learning to automatic parameter estimation,
ICPR16(444-449)
IEEE DOI 1705
Clustering algorithms, Kernel, Labeling, Parameter estimation, Shape, Static VAr compensators, Support vector machines BibRef

Riera, C.R.[Carles R.], Pujol, O.[Oriol],
An Approximate Support Vector Machines Solver with Budget Control,
CIARP16(377-384).
Springer DOI 1703
BibRef

Trichet, R.[Remi], O'Connor, N.E.[Noel E.],
A flexible ensemble-SVM for computer vision tasks,
AVSS16(51-58)
IEEE DOI 1611
Bagging BibRef

Elhoseiny, M.[Mohamed], Elgammal, A.M.[Ahmed M.],
Overlapping Domain Cover for Scalable and Accurate Regression Kernel Machines,
BMVC15(xx-yy).
DOI Link 1601
BibRef

Debnath, S.[Subhabrata], Banerjee, A.[Anjan], Namboodiri, V.[Vinay],
Adapting RANSAC SVM to Detect Outliers for Robust Classification,
BMVC15(xx-yy).
WWW Link. 1601
BibRef

Peters, E.[Ethan], Savakis, A.[Andreas],
SVM with OpenCL: High performance implementation of support vector machines on heterogeneous systems,
ICIP15(4322-4326)
IEEE DOI 1512
GPU Acceleration BibRef

Shi, X.S.[Xiao-Shuang], Guo, Z.H.[Zhen-Hua], Yang, Y.J.[Yu-Jiu], Yang, L.[Lin],
Within-class penalty based multi-class support vector machine,
ICIP15(2746-2750)
IEEE DOI 1512
SVM; multi-class; within-class scatter BibRef

Lu, S.X.[Shu-Xia], Tian, R.[Runa], Zhang, Y.F.[Yu-Fen],
A weighted least squares support vector machine based on covariance matrix,
ICWAPR15(192-197)
IEEE DOI 1511
covariance matrices BibRef

Reininghaus, J.[Jan], Huber, S.[Stefan], Bauer, U.[Ulrich], Kwitt, R.[Roland],
A stable multi-scale kernel for topological machine learning,
CVPR15(4741-4748)
IEEE DOI 1510
BibRef

Kobayashi, T.[Takumi],
Three viewpoints toward exemplar SVM,
CVPR15(2765-2773)
IEEE DOI 1510
BibRef

Rezende, R.S.[Rafael S.], Zepeda, J.[Joaquin], Ponce, J.[Jean], Bach, F.[Francis], Pérez, P.[Patrick],
Kernel Square-Loss Exemplar Machines for Image Retrieval,
CVPR17(7263-7271)
IEEE DOI 1711
Covariance matrices, Fasteners, Image retrieval, Kernel, Support vector machines, Training BibRef

Zepeda, J.[Joaquin], Perez, P.[Patrick],
Exemplar SVMs as visual feature encoders,
CVPR15(3052-3060)
IEEE DOI 1510
BibRef

Modolo, D.[Davide], Vezhnevets, A.[Alexander], Russakovsky, O.[Olga], Ferrari, V.[Vittorio],
Joint calibration of Ensemble of Exemplar SVMs,
CVPR15(3955-3963)
IEEE DOI 1510
BibRef

Shen, B.[Bin], Liu, B.D.[Bao-Di], Allebach, J.P.[Jan P.],
TISVM: Large margin classifier for misaligned image classification,
ICIP14(4251-4255)
IEEE DOI 1502
Computer vision BibRef

Zhang, G.P.[Guo-Peng], Piccardi, M.[Massimo],
Sequential Labeling with Structural SVM Under an Average Precision Loss,
SSSPR16(344-354).
Springer DOI 1611
BibRef
Earlier:
Sequential labeling with structural SVM under the F1 loss,
ICIP14(5272-5276)
IEEE DOI 1502
Accuracy BibRef

Liu, S.C.[Shu-Chun], Guo, J.[Jun], Zhong, S.Z.[Si-Zhi], Li, Y.F.[Yi-Fan],
A Novel Robust Modified Support Vector Machines,
ICPR14(3834-3838)
IEEE DOI 1412
Classification algorithms BibRef

Raval, N.[Nisarg], Tonge, R.[Rashmi], Jawahar, C.V.,
Efficient Evaluation of SVM Classifiers Using Error Space Encoding,
ICPR14(4411-4416)
IEEE DOI 1412
Accuracy BibRef

Lv, X.T.[Xu-Tao], Han, T.[Tony], Liu, Z.C.[Zi-Cheng], He, Z.H.[Zhi-Hai],
Randomized Support Vector Forest,
BMVC14(xx-yy).
HTML Version. 1410
BibRef

Nour-Eddine, L.[Lachachi], Abdelkader, A.[Adla],
Reduced Data Based Improved MEB/L2-SVM Equivalence,
MCPR14(1-10).
Springer DOI 1407
BibRef

Long, C.J.[Cheng-Jiang], Hua, G.[Gang],
Correlational Gaussian Processes for Cross-Domain Visual Recognition,
CVPR17(4932-4940)
IEEE DOI 1711
Gaussian processes, Image recognition, Pattern recognition, Random variables, Semantics, Tensile stress, Visualization
See also Joint Gaussian Process Model for Active Visual Recognition with Expertise Estimation in Crowdsourcing, A. BibRef

Long, C.J.[Cheng-Jiang], Hua, G.[Gang],
Multi-class Multi-annotator Active Learning with Robust Gaussian Process for Visual Recognition,
ICCV15(2839-2847)
IEEE DOI 1602
Bayes methods
See also Joint Gaussian Process Model for Active Visual Recognition with Expertise Estimation in Crowdsourcing, A. BibRef

Hua, G.[Gang], Long, C.J.[Cheng-Jiang], Yang, M.[Ming], Gao, Y.[Yan],
Collaborative Active Visual Recognition from Crowds: A Distributed Ensemble Approach,
PAMI(40), No. 3, March 2018, pp. 582-594.
IEEE DOI 1802
BibRef
Earlier:
Collaborative Active Learning of a Kernel Machine Ensemble for Recognition,
ICCV13(1209-1216)
IEEE DOI 1403
Collaboration, Crowdsourcing, Data models, Kernel, Labeling, Noise measurement, Visualization, Active learning, multiple oracles. BibRef

Sharma, G.[Gaurav], Jurie, F.[Frederic],
A Novel Approach for Efficient SVM Classification with Histogram Intersection Kernel,
BMVC13(xx-yy).
DOI Link 1402
BibRef

Shao, Y.H.[Yuan-Hai], Deng, N.Y.[Nai-Yang], Chen, W.J.[Wei-Jie], Wang, Z.[Zhen],
Improved Generalized Eigenvalue Proximal Support Vector Machine,
SPLetters(20), No. 3, March 2013, pp. 213-216.
IEEE DOI 1303
BibRef

Ren, Y.M.[Yue-Mei], Zhang, Y.N.[Yan-Ning], Meng, Q.J.[Qing-Jie], Zhang, L.[Lei],
Hyperspectral image classification based on Multiple Improved particle swarm cooperative optimization and SVM,
ICPR12(2274-2277).
WWW Link. 1302
BibRef

Seredin, O.[Oleg], Mottl, V.[Vadim], Tatarchuk, A.[Alexander], Razin, N.[Nikolay], Windridge, D.[David],
Convex support and Relevance Vector Machines for selective multimodal pattern recognition,
ICPR12(1647-1650).
WWW Link. 1302
BibRef

Xue, H.[Hui], Chen, S.C.[Song-Can], Huang, J.J.[Ji-Jian],
Discriminative indefinite kernel classifier from pairwise constraints and unlabeled data,
ICPR12(497-500).
WWW Link. 1302
BibRef

Ji, Y.[You], Sun, S.L.[Shi-Liang], Lu, Y.[Yue],
Multitask multiclass privileged information support vector machines,
ICPR12(2323-2326).
WWW Link. 1302
BibRef

Litayem, S.[Saloua], Joly, A.[Alexis], Boujemaa, N.[Nozha],
Hash-Based Support Vector Machines Approximation for Large Scale Prediction,
BMVC12(86).
DOI Link 1301
BibRef

Darkner, S.[Sune], Clemmensen, L.H.[Line H.],
Data Driven Constraints for the SVM,
MLMI12(70-77).
Springer DOI 1211
BibRef

Bazavan, E.G.[Eduard Gabriel], Li, F.X.[Fu-Xin], Sminchisescu, C.[Cristian],
Fourier Kernel Learning,
ECCV12(II: 459-473).
Springer DOI 1210
BibRef

González, F.A.[Fabio A.], Bermeo, D.[David], Ramos, L.[Laura], Nasraoui, O.[Olfa],
On the Robustness of Kernel-based Clustering,
CIARP12(122-129).
Springer DOI 1209
BibRef

Cohen, D.A.[Diego Arab], Fernández, E.A.[Elmer Andrés],
SVMTOCP: A Binary Tree Base SVM Approach through Optimal Multi-class Binarization,
CIARP12(472-478).
Springer DOI 1209
BibRef

Porro-Muñoz, D.[Diana], Duin, R.P.W.[Robert P.W.], Talavera, I.[Isneri],
Missing Values in Dissimilarity-Based Classification of Multi-way Data,
CIARP13(I:214-221).
Springer DOI 1311
BibRef

Hernández, N.[Noslen], Biscay, R.J.[Rolando J.], Talavera, I.[Isneri],
A Non Bayesian Predictive Approach for Functional Calibration,
CIARP12(781-788).
Springer DOI 1209
BibRef
Earlier:
Support Vector Regression Methods for Functional Data,
CIARP07(564-573).
Springer DOI 0711
BibRef

Hernández, N.[Noslen], Biscay, R.J.[Rolando J.], Villa-Vialaneix, N.[Nathalie], Talavera, I.[Isneri],
A Functional Density-Based Nonparametric Approach for Statistical Calibration,
CIARP10(450-457).
Springer DOI 1011
BibRef

Veon, K.L.[Kevin L.], Mahoor, M.H.[Mohammad H.],
Localized support vector machines using Parzen window for incomplete sets of categories,
WACV11(448-454).
IEEE DOI 1101
Deal with objects that are undefined. BibRef

Wu, J.[Jun], Lin, Z.K.[Zheng-Kui], Lu, M.Y.[Ming-Yu],
Asymmetric semi-supervised boosting for SVM active learning in CBIR,
CIVR10(182-188).
DOI Link 1007
BibRef

Gripton, A.[Adam], Lu, W.P.[Wei-Ping],
Kernel Domain Description with Incomplete Data: Using Instance-Specific Margins to Avoid Imputation,
ICPR10(2921-2924).
IEEE DOI 1008
BibRef

Gao, J.[Jun], Hu, W.M.[Wei-Ming], Li, W.[Wei], Zhang, Z.F.M.[Zhong-Fei Mark], Wu, O.[Ou],
Local Outlier Detection Based on Kernel Regression,
ICPR10(585-588).
IEEE DOI 1008
BibRef

He, H.[He], Ghodsi, A.[Ali],
Rare Class Classification by Support Vector Machine,
ICPR10(548-551).
IEEE DOI 1008
BibRef

Li, J.[Jinbo], Sun, S.L.[Shi-Liang],
Nonlinear Combination of Multiple Kernels for Support Vector Machines,
ICPR10(2889-2892).
IEEE DOI 1008
BibRef

Wu, J.[Jun], Lu, M.Y.[Ming-Yu], Wang, C.L.[Chun-Li],
Enhancing SVM Active Learning for Image Retrieval Using Semi-supervised Bias-Ensemble,
ICPR10(3175-3178).
IEEE DOI 1008
BibRef

Khan, N.M.[Naimul Mefraz], Ksantini, R.[Riadh], Ahmad, I.S.[Imran Shafiq], Boufama, B.[Boubaker],
A New SVM + NDA Model for Improved Classification and Recognition,
ICIAR10(I: 127-136).
Springer DOI 1006
BibRef

Chen, S.[Shuo], Zhang, C.S.[Chang-Shui],
Image classification via SVM using in-between universum samples,
ICIP09(1421-1424).
IEEE DOI 0911
I.e. samples that do not belong to any task related classes. BibRef

Banki, M.H.[Mohammad Hossein], Shirazi, A.A.B.[Ali Asghar Beheshti],
Using Wavelet Support Vector Machine for Classification of Hyperspectral Images,
ICMV09(154-157).
IEEE DOI 0912
BibRef

Kim, J.[Junae], Shen, C.H.[Chun-Hua], Wang, L.[Lei],
Learning Cascaded Reduced-Set SVMs Using Linear Programming,
DICTA08(619-626).
IEEE DOI 0812
BibRef

Sun, Z.C.[Zhi-Chao], Liu, Z.G.[Zhi-Gang], Liu, S.H.[Su-Hong], Zhang, Y.[Yun], Yang, B.[Bing],
Active Learning with Support Vector Machines in Remotely Sensed Image Classification,
CISP09(1-6).
IEEE DOI 0910
BibRef

Hai-Yuan, L.[Liu], Sun, J.C.[Jian-Cheng],
A Modulation Type Recognition Method Using Wavelet Support Vector Machines,
CISP09(1-4).
IEEE DOI 0910
BibRef

Nowozin, S.[Sebastian], Gehler, P.V.[Peter V.], Lampert, C.H.[Christoph H.],
On Parameter Learning in CRF-Based Approaches to Object Class Image Segmentation,
ECCV10(VI: 98-111).
Springer DOI 1009
BibRef

Gehler, P.V.[Peter Vincent], Nowozin, S.[Sebastian],
On Feature Combination for Multiclass Object Classification,
ICCV09(221-228).
IEEE DOI 0909
BibRef
And:
Let the kernel figure it out; Principled learning of pre-processing for kernel classifiers,
CVPR09(2836-2843).
IEEE DOI 0906
BibRef

Basak, J.[Jayanta],
A least square kernel machine with box constraints,
ICPR08(1-4).
IEEE DOI 0812
BibRef

Alpcan, T.[Tansu], Bauckhage, C.[Christian],
A discrete-time parallel update algorithm for distributed learning,
ICPR08(1-4).
IEEE DOI 0812
BibRef

Ilayaraja, P., Neeba, N.V., Jawahar, C.V.,
Efficient implementation of SVM for large class problems,
ICPR08(1-4).
IEEE DOI 0812
BibRef

Nishida, K.[Kenji], Fujiki, J.[Jun], Kurita, T.[Takio],
Multiple Random Subset-Kernel Learning,
CAIP11(I: 343-350).
Springer DOI 1109
BibRef

Nishida, K.[Kenji], Kurita, T.[Takio],
RANSAC-SVM for large-scale datasets,
ICPR08(1-4).
IEEE DOI 0812
BibRef

Tatarchuk, A., Mottl, V., Eliseyev, A., Windridge, D.,
Selectivity supervision in combining pattern-recognition modalities by feature- and kernel-selective support vector machines,
ICPR08(1-4).
IEEE DOI 0812
BibRef

Mao, W.T.[Wen-Tao], Dong, L.L.[Long-Lei], Zhang, G.[Gang],
Weighted solution path algorithm of support vector regression for abnormal data,
ICPR08(1-4).
IEEE DOI 0812
BibRef

Yu, X.D.[Xiao-Dong], DeMenthon, D.F.[Daniel F.], Doermann, D.[David],
Support Vector Data Description for image categorization from Internet images,
ICPR08(1-4).
IEEE DOI 0812
BibRef

Lienemann, K.[Kai], Plotz, T.[Thomas], Fink, G.A.[Gernot A.],
SVM ensemble classification of NMR spectra based on different configurations of data processing techniques,
ICPR08(1-4).
IEEE DOI 0812
BibRef

Habib, T.[Tarek], Inglada, J.[Jordi], Mercier, G.[Gregoire], Chanussot, J.[Jocelyn],
Speeding up Support Vector Machine (SVM) image classification by a kernel series expansion,
ICIP08(865-868).
IEEE DOI 0810

See also New Statistical Similarity Measure for Change Detection in Multitemporal SAR Images and Its Extension to Multiscale Change Analysis, A. BibRef

Sadeghi, M.T.[Mohammad T.], Samiei, M.[Masoumeh], Kittler, J.V.[Josef V.],
Selection and Fusion of Similarity Measure Based Classifiers Using Support Vector Machines,
SSPR08(479-488).
Springer DOI 0812
BibRef

Schnitzspan, P.[Paul], Fritz, M.[Mario], Roth, S.[Stefan], Schiele, B.[Bernt],
Discriminative structure learning of hierarchical representations for object detection,
CVPR09(2238-2245).
IEEE DOI 0906
BibRef

Demirkesen, C.[Can], Cherifi, H.[Hocine],
A Comparison of Multiclass SVM Methods for Real World Natural Scenes,
ACIVS08(xx-yy).
Springer DOI 0810
BibRef

Hazan, T.[Tamir], Man, A.[Amit], Shashua, A.[Amnon],
A Parallel Decomposition Solver for SVM: Distributed dual ascend using Fenchel Duality,
CVPR08(1-8).
IEEE DOI 0806
BibRef

Maji, S.[Subhransu],
Linearized Smooth Additive Classifiers,
WebScale12(I: 239-248).
Springer DOI 1210
BibRef

Maji, S.[Subhransu], Berg, A.C.[Alexander C.],
Max-margin additive classifiers for detection,
ICCV09(40-47).
IEEE DOI 0909
BibRef

Maji, S.[Subhransu], Berg, A.C.[Alexander C.], Malik, J.[Jitendra],
Classification using intersection kernel support vector machines is efficient,
CVPR08(1-8).
IEEE DOI 0806
BibRef

Bristow, H.[Hilton], Lucey, S.[Simon],
V1-Inspired Features Induce a Weighted Margin in SVMs,
ECCV12(II: 59-72).
Springer DOI 1210
BibRef

Lucey, S.[Simon],
Enforcing non-positive weights for stable support vector tracking,
CVPR08(1-8).
IEEE DOI 0806
BibRef

Sluzhivoy, A.[Andrey], Pauli, J.[Josef], Rölke, V.[Volker], Noglik, A.[Anastasia],
Improving the Run-Time Performance of Multi-class Support Vector Machines,
DAGM08(xx-yy).
Springer DOI 0806
BibRef

Varma, C.M.B.S.[C.M.B. Seshikanth], Asharaf, S., Murty, M.N.[M. Narasimha],
Rough Core Vector Clustering,
PReMI07(304-310).
Springer DOI 0712
BibRef

Kong, X.D.[Xiao-Dong], Luo, Q.S.[Qing-Shan], Zeng, G.H.[Gui-Hua],
A Novel SVM-Based Method for Moving Video Objects Recognition,
Visual07(136-145).
Springer DOI 0706
BibRef

Ionescu, C.[Catalin], Bo, L.F.[Lie-Feng], Sminchisescu, C.[Cristian],
Structural SVM for visual localization and continuous state estimation,
ICCV09(1157-1164).
IEEE DOI 0909
BibRef

Kumar, A.[Ankita], Sminchisescu, C.[Cristian],
Support Kernel Machines for Object Recognition,
ICCV07(1-8).
IEEE DOI 0710
BibRef

Moguerza, J.M.[Javier M.], Muñoz, A.[Alberto], Psarakis, S.[Stelios],
Monitoring Nonlinear Profiles Using Support Vector Machines,
CIARP07(574-583).
Springer DOI 0711
BibRef

Chatelain, C., Adam, S., Lecourtier, Y., Heutte, L., Paquet, T.,
Multi-Objective Optimization for SVM Model Selection,
ICDAR07(427-431).
IEEE DOI 0709
BibRef

Karim, R.[Rezaul], Bergtholdt, M.[Martin], Kappes, J.H.[Jörg H.], Schnörr, C.[Christoph],
Greedy-Based Design of Sparse Two-Stage SVMs for Fast Classification,
DAGM07(395-404).
Springer DOI 0709
BibRef

Tanaka, A.[Akira], Takigawa, I.[Ichigaku], Imai, H.[Hideyuki], Kudo, M.[Mineichi],
Analyses on Generalization Error of Ensemble Kernel Regressors,
SSSPR14(273-281).
Springer DOI 1408
BibRef
Earlier:
Extended Analyses for an Optimal Kernel in a Class of Kernels with an Invariant Metric,
SSSPR12(345-353).
Springer DOI 1211
BibRef

Tanaka, A.[Akira], Imai, H.[Hideyuki], Kudo, M.[Mineichi], Miyakoshi, M.[Masaaki],
A Relationship Between Generalization Error and Training Samples in Kernel Regressors,
ICPR10(1421-1424).
IEEE DOI 1008
BibRef
And:
Optimal Kernel in a Class of Kernels with an Invariant Metric,
SSPR08(530-539).
Springer DOI 0812
BibRef

Tanaka, A.[Akira], Sugiyama, M.[Masashi], Imai, H.[Hideyuki], Kudo, M.[Mineichi], Miyakoshi, M.[Masaaki],
Model Selection Using a Class of Kernels with an Invariant Metric,
SSPR06(862-870).
Springer DOI 0608
BibRef

Joachims, T.[Thorsten],
Structured Output Prediction with Support Vector Machines,
SSPR06(1-7).
Springer DOI 0608
BibRef

Zhu, Y.S.[Yong-Sheng], Yang, J.Y.[Jun-Yan], Ye, J.[Jian], Zhang, Y.Y.[You-Yun],
A Speedup Method for SVM Decision,
SSPR06(494-501).
Springer DOI 0608
BibRef

Zhang, R., Metaxas, D.,
RO-SVM: Support Vector Machine with Reject Option for Image Categorization,
BMVC06(III:1209).
PDF File. 0609
BibRef

Tzotsos, A.,
A support vector machine approach for object based image analysis,
OBIA06(xx-yy).
PDF File. 0607
BibRef

Liu, Y.[Yi], Zheng, Y.F.[Yuan F.],
Minimum Enclosing and Maximum Excluding Machine for Pattern Description and Discrimination,
ICPR06(III: 129-132).
IEEE DOI 0609
Extend SVM to enable rejection of out of class. BibRef

Kropotov, D.[Dmitry], Ptashko, N.[Nikita], Vasiliev, O.[Oleg], Vetrov, D.[Dmitry],
On Kernel Selection in Relevance Vector Machines Using Stability Principle,
ICPR06(IV: 233-236).
IEEE DOI 0609
BibRef

Qin, J.Z.[Jian-Zhao], Li, Y.Q.[Yuan-Qing],
An Improved Semi-Supervised Support Vector Machine Based Translation Algorithm for BCI Systems,
ICPR06(I: 1240-1243).
IEEE DOI 0609
BibRef

Chen, G.Y., Bhattacharya, P.,
Function Dot Product Kernels for Support Vector Machine,
ICPR06(II: 614-617).
IEEE DOI 0609
BibRef

Ye, N.[Ning], Sun, R.X.[Rui-Xiang], Liu, Y.G.[Yin-Gan], Cao, L.[Lin],
Support vector machine with orthogonal Chebyshev kernel,
ICPR06(II: 752-755).
IEEE DOI 0609
BibRef

Barakat, N.[Nahla], Bradley, A.P.[Andrew P.],
Rule Extraction from Support Vector Machines: Measuring the Explanation Capability Using the Area under the ROC Curve,
ICPR06(II: 812-815).
IEEE DOI 0609
BibRef

Zhang, X.F.[Xian-Fei], Li, B.C.[Bi-Cheng], Shi, W.[Wang], Cheng, L.[Luo],
An Efficient SVM Classifier for Lopsided Corpora,
ICPR06(I: 1144-1147).
IEEE DOI 0609
BibRef

Sung, E.[Eric], Yan, Z.[Zhu], Li, X.C.[Xu-Chun],
Accelerating the SVM Learning for Very Large Data Sets,
ICPR06(II: 484-489).
IEEE DOI 0609
BibRef

Wu, Z.L.[Zhi-Li], Li, C.H.[Chun-Hung], Zhu, J.[Ji], Huang, J.[Jian],
A Semi-supervised SVM for Manifold Learning,
ICPR06(II: 490-493).
IEEE DOI 0609
BibRef

Arreola, K.Z.[Karina Zapien], Fehr, J.[Janis], Burkhardt, H.[Hans],
Fast Support Vector Machine Classification using linear SVMs,
ICPR06(III: 366-369).
IEEE DOI 0609
BibRef

Zhan, Y.Q.[Yi-Qiang], Shen, D.G.[Ding-Gang],
Increasing Efficiency of SVM by Adaptively Penalizing Outliers,
EMMCVPR05(539-551).
Springer DOI 0601
BibRef

Tung, J.W.[Jia-Wen], Hsu, C.T.[Chiou-Ting],
Learning Hidden Semantic Cues Using Support Vector Clustering,
ICIP05(I: 1189-1192).
IEEE DOI 0512
BibRef

Kahsay, L.[Laine], Schwenker, F.[Friedhelm], Palm, G.[Günther],
Comparison of Multiclass SVM Decomposition Schemes for Visual Object Recognition,
DAGM05(334).
Springer DOI 0509
BibRef

Lyu, S.W.[Si-Wei],
Mercer Kernels for Object Recognition with Local Features,
CVPR05(II: 223-229).
IEEE DOI 0507
BibRef

Sun, Q.A.[Qi-Ang], DeJong, G.[Gerald],
Feature Kernel Functions: Improving SVMs Using High-Level Knowledge,
CVPR05(II: 177-183).
IEEE DOI 0507
BibRef

Tarel, J.P.[Jean-Philippe], Boughorbel, S.[Sabri],
Object Predetection Based on Kernel Parametric Distribution Fitting,
ICPR06(II: 808-811).
IEEE DOI 0609
BibRef

Boughorbel, S., Tarel, J.P., Boujemaa, N.,
Generalized Histogram Intersection Kernel for Image Recognition,
ICIP05(III: 161-164).
IEEE DOI 0512
BibRef

Boughorbel, S., Tarel, J.P., Fleuret, F.,
Non-Mercer Kernels for SVM Object Recognition,
BMVC04(xx-yy).
HTML Version. 0508
BibRef

Wang, Y.C.A.[Yu-Chi-Ang], Casasent, D.,
A hierarchical classifier using new support vector machine,
ICDAR05(II: 851-855).
IEEE DOI 0508
BibRef

Yu, W.M.[Wei Miao], Du, T.H.[Tie-Hua], Lim, K.B.[Kah Bin],
Comparison of the support vector machine and relevant vector machine in regression and classification problems,
ICARCV04(II: 1309-1314).
IEEE DOI 0412
BibRef

Man, H.[Hong], Chen, L.[Ling], Duan, R.[Rong],
Rotation invariant texture classification using directional filter bank and support vector machine,
ICIP04(III: 1545-1548).
IEEE DOI 0505
BibRef

Hein, M.[Matthias], Lal, T.N.[Thomas Navin], Bousquet, O.[Olivier],
Hilbertian Metrics on Probability Measures and Their Application in SVMs,
DAGM04(270-277).
Springer DOI 0505
BibRef

Horikawa, Y.[Yo],
Comparison of support vector machines with autocorrelation kernels for invariant texture classification,
ICPR04(I: 660-663).
IEEE DOI 0409
BibRef

Park, J.H.[Jin-Hyeong], Ji, X.[Xiang], Zha, H.Y.[Hong-Yuan], Kasturi, R.,
Support vector clustering combined with spectral graph partitioning,
ICPR04(IV: 581-584).
IEEE DOI 0409
BibRef

Imbault, F., Lebart, K.,
A stochastic optimization approach for parameter tuning of support vector machines,
ICPR04(IV: 597-600).
IEEE DOI 0409
BibRef

Hoi, S.C.H.[Steven C. H.], Lyu, M.R.[Michael R.],
Group-based relevance feedback with support vector machine ensembles,
ICPR04(III: 874-877).
IEEE DOI 0409
BibRef

Chen, J.H.[Jiun-Hung],
M-estimator based robust kernels for support vector machines,
ICPR04(I: 168-171).
IEEE DOI 0409
BibRef

Gokcen, I., Joachim, D., Deller, J.R.,
Comparing optimal bounding ellipsoid and support vector machine active learning,
ICPR04(I: 172-175).
IEEE DOI 0409
BibRef

Zhang, P.[Peng], Peng, J.[Jing], Riedel, N.,
Discriminant Analysis: A Least Squares Approximation View,
LCV05(III: 46-46).
IEEE DOI 0507
BibRef

Zhang, P.[Peng], Peng, J.[Jing],
Efficient Regularized Least Squares Classification,
LCV04(98).
IEEE DOI 0406
BibRef
And:
SVM vs regularized least squares classification,
ICPR04(I: 176-179).
IEEE DOI 0409
BibRef

Tortorella, F.,
An empirical comparison of in-learning and post-learning optimization schemes for tuning the support vector machines in cost-sensitive applications,
CIAP03(560-566).
IEEE DOI 0310
BibRef

Yuan, C.[Chao], Casasent, D.,
A novel support vector classifier with better rejection performance,
CVPR03(I: 419-424).
IEEE DOI 0307
BibRef

Vishwanathan, S.V.N., Murty, M.N.,
Geometric SVM: a fast and intuitive SVM algorithm,
ICPR02(II: 56-59).
IEEE DOI 0211
BibRef

Xiao, X.[Xipan], Ai, H.Z.[Hai-Zhou], Xu, G.Y.[Guang-You],
Pair-wise sequential reduced set for optimization of support vector machines,
ICPR02(II: 860-863).
IEEE DOI 0211
BibRef

Zhong, S.W.[Sheng-Wei], Chang, C.I.[Chein-I], Zhang, Y.[Ye],
Iterative Support Vector Machine for Hyperspectral Image Classification,
ICIP18(3309-3312)
IEEE DOI 1809
Hyperspectral imaging, Support vector machines, Image classification, Information filters, hyperspectral image classification BibRef

Zhang, J.P.[Jun-Ping], Zhang, Y.[Ye], Zhou, T.X.[Ting-Xian],
Classification of Hyperspectral Data Using Support Vector Machine,
ICIP01(I: 882-885).
IEEE DOI 0108
BibRef

Nakamura, E., Murayama, N., Sawada, K., Okuizumi, H.,
RLGS Profile Segmentation Via a SVM,
ICIP01(I: 533-536).
IEEE DOI 0108
BibRef

Ben-Hur, A., Horn, D., Siegelmann, H.T., Vapnik, V.,
A Support Vector Clustering Method,
ICPR00(Vol II: 724-727).
IEEE DOI 0009
BibRef

Odone, F., Trucco, M., Verri, A.,
Visual Learning of Weight from Shape Using Support Vector Machines,
BMVC98(xx-yy). BibRef 9800

Pawlak, M., Ng, M.F.Y.F.[M.F. Yat Fung],
On kernel and radial basis function techniques for classification and function recovering,
ICPR94(B:454-456).
IEEE DOI 9410
BibRef

Pawlak, M., Siedlecki, W.,
Kernel classification rules in the presence of missing values,
ICPR90(I: 677-680).
IEEE DOI 9006
BibRef

Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Training Support Vector Machines, SVM Training, Learning .


Last update:Mar 16, 2024 at 20:36:19