14.2.20 Support Vector Machines, SVM

, V.N.V.[Vladimir N. Vapnik],
Statistical Learning Theory,
WileySeptember 1998. ISBN: 978-0-471-03003-4 The source of the Support Vector Machine ideas. BibRef 9809

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,
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Huang, K.Z.[Kai-Zhu], Zheng, D.N.[Da-Nian], Sun, J.[Jun], Hotta, Y.[Yoshinobu], Fujimoto, K.[Katsuhito], Naoi, S.[Satoshi],
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Sparse representation; Implementations of L0-norm; Regularization term; Support vector machine; Kernel methods BibRef

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Han, D.Q.[De-Qiang], Han, C.Z.[Chong-Zhao], Yang, Y.[Yi],
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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],
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Confidence; Hidden classes; Multi-class classification; Smooth method; Support vector machine; Ternary voting games BibRef

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IEICE(E94-D), No. 2, February 2011, pp. 379-383.
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Context-dependent kernel design for object matching and recognition,
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Scale-Invariance of Support Vector Machines based on the Triangular Kernel,
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Jiu, M., Sahbi, H.[Hichem],
Nonlinear Deep Kernel Learning for Image Annotation,
IP(26), No. 4, April 2017, pp. 1820-1832.
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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,
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Ozer, S.[Sedat],
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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],
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PR(44), No. 10-11, October-November 2011, pp. 2375-2383.
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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],
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PR(44), No. 10-11, October-November 2011, pp. 2643-2655.
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Multiple-surface classifier; Twin support vector machine; Quadratic programming BibRef

Wittek, P.[Peter], Tan, C.L.[Chew Lim],
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Laanaya, H.[Hicham], Abdallah, F.[Fahed], Snoussi, H.[Hichem], Richard, C.[Cédric],
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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.,
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IEEE DOI 1208
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Wu, J.X.[Jian-Xin],
Efficient HIK SVM Learning for Image Classification,
IP(21), No. 10, October 2012, pp. 4442-4453.
IEEE DOI 1209
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Power mean SVM for large scale visual classification,
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IEEE DOI 1208
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Earlier:
A Fast Dual Method for HIK SVM Learning,
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Wang, C.D.[Chang-Dong], Lai, J.H.[Jian-Huang],
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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.,
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1-v-r SVM; Convex hull; Kernel methods; Multiclassification BibRef

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Probabilistic expression of Polynomial Semantic Indexing and its application for classification,
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Polynomial Semantic Indexing BibRef

Abrahamsen, T.J.[Trine Julie], Hansen, L.K.[Lars Kai],
Variance inflation in high dimensional Support Vector Machines,
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Elsevier DOI 1402
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Clustering large datasets with kernel methods,
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Cheng, Q.A.[Qi-Ang], Tezcan, J.[Jale], Cheng, J.[Jie],
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Elsevier DOI 1404
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Chen, W.J.[Wei-Jie], Shao, Y.H.[Yuan-Hai], Li, C.N.[Chun-Na], Deng, N.Y.[Nai-Yang],
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Multi-label classification BibRef

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Support vector machine BibRef

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Feature extraction BibRef

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Twin support vector machine BibRef

Peng, S.L.[Shi-Li], Hu, Q.H.[Qing-Hua], Chen, Y.L.[Yin-Li], Dang, J.[Jianwu],
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Elsevier DOI 1503
Support vector machine BibRef

Chen, J.H.[Jin-Hui], Takiguchi, T.[Tetsuya], Ariki, Y.[Yasuo],
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Cybernetics BibRef

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Multi-task learning BibRef

Bae, J.S.[Ji-Sang], Kim, J.O.[Jong-Ok],
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Gaussian kernel clustering, Kernelization of the metric, Feature space, Width hyper-parameter BibRef

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Support Vector Machine (SVM) BibRef

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Support vector machine BibRef

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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.,
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IEEE DOI 2001
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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],
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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],
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PR(70), No. 1, 2017, pp. 177-184.
Elsevier DOI 1706
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Elsevier DOI 1710
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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],
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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],
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Elsevier DOI 1712
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Zheng, Q.Q.[Qing-Qing], Zhu, F.Y.[Feng-Yuan], Qin, J.[Jing], Chen, B.D.[Ba-Dong], Heng, P.A.[Pheng-Ann],
Sparse Support Matrix Machine,
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Chen, G.[Gang], Xu, R.[Ran], Yang, Z.[Zhi],
Deep ranking structural support vector machine for image tagging,
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Image tagging, Maximum margin learning, Deep learning, Ranking BibRef

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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],
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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],
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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?,
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Elsevier DOI 1812
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Ma, J.J.[Jia-Jun], Zhou, S.[Shuisheng], Chen, L.[Li], Wang, W.W.[Wei-Wei], Zhang, Z.[Zhuan],
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Elsevier DOI 1901
SVM, Multi-class classification, Outliers, K-SVCR, Sparse solution BibRef

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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

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Support vector data description (SVDD), Data distribution, Sample reduction, Farthest boundary point BibRef

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Deep support vector machine for hyperspectral image classification,
PR(103), 2020, pp. 107298.
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Remote sensing, Hyperspectral image, Deep support vector machine, Image classification BibRef

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Asynchronous parallel, Coordinate descent, Support vector, Ordinal regression BibRef

Sohrab, F.[Fahad], Raitoharju, J.[Jenni], Iosifidis, A.[Alexandros], Gabbouj, M.[Moncef],
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PR(110), 2021, pp. 107648.
Elsevier DOI 2011
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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],
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PR(133), 2023, pp. 108999.
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One-Class classification, Support vector data description, Subspace learning, Spectral regression BibRef

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Support vector data description, SVDD, One-class support vector machines, OCSVM, Gaussian kernel, Gaussian kernel bandwidth BibRef

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Support Vector Machine-Based Blind Equalization for High-Order QAM With Short Data Length,
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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,
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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

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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.
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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.
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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.
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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.W.[Yi-Wen], 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.
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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.
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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
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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

Sun, H.T.[He-Ting], Wang, L.G.[Li-Guo], Liu, H.T.[Hai-Tao], Sun, Y.[Yinbang],
Hyperspectral Image Classification with the Orthogonal Self-Attention ResNet and Two-Step Support Vector Machine,
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Wang, H.J.[Hua-Jun], Zhang, H.W.[Hong-Wei], Li, W.Q.[Wen-Qian],
Sparse and robust support vector machine with capped squared loss for large-scale pattern classification,
PR(153), 2024, pp. 110544.
Elsevier DOI 2405
Capped squared loss, Fast algorithm, Support vectors, Low computational complexity, Working set BibRef

Yang, Z.J.[Zhi-Ji], Chen, W.[Wanyi], Zhang, H.[Huan], Xu, Y.T.[Yi-Tian], Shi, L.[Lei], Zhao, J.H.[Jian-Hua],
A safe screening rule with bi-level optimization of v support vector machine,
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Elsevier DOI 2408
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Lu, J.Q.[Jun-Qi], Xie, X.J.[Xi-Jiong], Xiong, Y.J.[Yu-Jie],
Multi-view hypergraph regularized Lp norm least squares twin support vector machines for semi-supervised learning,
PR(156), 2024, pp. 110753.
Elsevier DOI 2408
Multi-view semi-supervised learning, Twin support vector machines, Lp norm graph regularization, Hypergraph regularized BibRef

Liu, S.[Shiyao], Yan, B.R.[Bao-Rong], Guo, W.[Wei], Hua, Y.[Yu], Zhang, S.G.[Shou-Gang], Lu, J.[Jun], Xu, L.[Lu], Yang, D.[Dong],
Research on ELoran Demodulation Algorithm Based on Multiclass Support Vector Machine,
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Sparsification of Indefinite Learning Models,
SSSPR18(173-183).
Springer DOI 1810
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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,
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Slack and Margin Rescaling as Convex Extensions of Supermodular Functions,
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Sample-based regularization for support vector machine classification,
IPTA17(1-6)
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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
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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,
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ICIP15(4322-4326)
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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)
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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
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Kobayashi, T.[Takumi],
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CVPR15(2765-2773)
IEEE DOI 1510
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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
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Modolo, D.[Davide], Vezhnevets, A.[Alexander], Russakovsky, O.[Olga], Ferrari, V.[Vittorio],
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CVPR15(3955-3963)
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Shen, B.[Bin], Liu, B.D.[Bao-Di], Allebach, J.P.[Jan P.],
TISVM: Large margin classifier for misaligned image classification,
ICIP14(4251-4255)
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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
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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)
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Raval, N.[Nisarg], Tonge, R.[Rashmi], Jawahar, C.V.,
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ICPR14(4411-4416)
IEEE DOI 1412
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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,
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Shao, Y.H.[Yuan-Hai], Deng, N.Y.[Nai-Yang], Chen, W.J.[Wei-Jie], Wang, Z.[Zhen],
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Ren, Y.M.[Yue-Mei], Zhang, Y.N.[Yan-Ning], Meng, Q.J.[Qing-Jie], Zhang, L.[Lei],
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González, F.A.[Fabio A.], Bermeo, D.[David], Ramos, L.[Laura], Nasraoui, O.[Olfa],
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Support Vector Regression Methods for Functional Data,
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Hernández, N.[Noslen], Biscay, R.J.[Rolando J.], Villa-Vialaneix, N.[Nathalie], Talavera, I.[Isneri],
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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
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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
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Zhang, R., Metaxas, D.,
RO-SVM: Support Vector Machine with Reject Option for Image Categorization,
BMVC06(III:1209).
PDF File. 0609
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Tzotsos, A.,
A support vector machine approach for object based image analysis,
OBIA06(xx-yy).
PDF File. 0607
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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
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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
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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
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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
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Zhan, Y.Q.[Yi-Qiang], Shen, D.G.[Ding-Gang],
Increasing Efficiency of SVM by Adaptively Penalizing Outliers,
EMMCVPR05(539-551).
Springer DOI 0601
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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 .