Lau, K.W.,
Wu, Q.H.,
Online training of support vector classifier,
PR(36), No. 8, August 2003, pp. 1913-1920.
Elsevier DOI
0304
BibRef
Lau, K.W.,
Wu, Q.H.,
Leave one support vector out cross validation for fast estimation of
generalization errors,
PR(37), No. 9, September 2004, pp. 1835-1840.
Elsevier DOI
0407
BibRef
Katagiri, S.[Shinya],
Abe, S.[Shigeo],
Incremental training of support vector machines using hyperspheres,
PRL(27), No. 13, 1 October 2006, pp. 1495-1507.
Elsevier DOI Incremental training; Hyperspheres;
0606
BibRef
Abe, S.[Shigeo],
Fuzzy support vector machines for multilabel classification,
PR(48), No. 6, 2015, pp. 2110-2117.
Elsevier DOI
1503
Multilabel classification
BibRef
Cheng, S.X.[Shou-Xian],
Shih, F.Y.[Frank Y.],
An improved incremental training algorithm for support vector machines
using active query,
PR(40), No. 3, March 2007, pp. 964-971.
Elsevier DOI
0611
Incremental training; Active learning; Support vector machine;
Clustering algorithm; Pattern classification
See also Improved feature reduction in input and feature spaces.
BibRef
Carvalho, B.P.R.,
Braga, A.P.,
IP-LSSVM: A two-step sparse classifier,
PRL(30), No. 16, 1 December 2009, pp. 1507-1515.
Elsevier DOI
0911
Sparse classifier; Least squares support vector machine; Support
vector automatic detection
BibRef
Duan, H.[Hua],
Shao, X.J.[Xiao-Jian],
Hou, W.Z.[Wei-Zhen],
He, G.P.[Guo-Ping],
Zeng, Q.T.[Qing-Tian],
An incremental learning algorithm for Lagrangian support vector
machines,
PRL(30), No. 15, 1 November 2009, pp. 1384-1391.
Elsevier DOI
0910
Lagrangian; Support vector machines; Incremental learning; Online learning
BibRef
Pronobis, A.[Andrzej],
Jie, L.[Luo],
Caputo, B.[Barbara],
The more you learn, the less you store:
Memory-controlled incremental SVM for visual place recognition,
IVC(28), No. 7, July 2010, pp. 1080-1097.
Elsevier DOI
1006
Localization. Incremental learning; Knowledge transfer; Support vector machines;
Place recognition; Visual robot localization
BibRef
He, X.S.[Xi-Sheng],
Wang, Z.[Zhe],
Jin, C.[Cheng],
Zheng, Y.B.[Ying-Bin],
Xue, X.Y.[Xiang-Yang],
A simplified multi-class support vector machine with reduced dual
optimization,
PRL(33), No. 1, 1 January 2012, pp. 71-82.
Elsevier DOI
1112
Multi-class classification; Support vector machine; Kernel-based
methods; Pattern classification
BibRef
Nikitidis, S.[Symeon],
Nikolaidis, N.[Nikos],
Pitas, I.[Ioannis],
Multiplicative update rules for incremental training of multiclass
support vector machines,
PR(45), No. 5, May 2012, pp. 1838-1852.
Elsevier DOI
1201
BibRef
Earlier:
Incremental Training of Multiclass Support Vector Machines,
ICPR10(4267-4270).
IEEE DOI
1008
Support vector machines; Online training; Incremental learning;
Quadratic programming; Warm-start algorithm
BibRef
Qi, Z.Q.[Zhi-Quan],
Tian, Y.J.[Ying-Jie],
Shi, Y.[Yong],
Robust twin support vector machine for pattern classification,
PR(46), No. 1, January 2013, pp. 305-316.
Elsevier DOI
1209
Award, Pattern Recognition. Classification; Twin support vector machine; Second order cone
programming; Robust
BibRef
Tian, Y.J.[Ying-Jie],
Qi, Z.Q.[Zhi-Quan],
Ju, X.,
Shi, Y.[Yong],
Liu, X.,
Nonparallel Support Vector Machines for Pattern Classification,
Cyber(44), No. 7, July 2014, pp. 1067-1079.
IEEE DOI
1407
Cybernetics
BibRef
Chen, D.D.[Dan-Dan],
Tian, Y.J.[Ying-Jie],
Liu, X.H.[Xiao-Hui],
Structural nonparallel support vector machine for pattern recognition,
PR(60), No. 1, 2016, pp. 296-305.
Elsevier DOI
1609
Structural information
BibRef
Shen, X.[Xin],
Niu, L.F.[Ling-Feng],
Qi, Z.Q.[Zhi-Quan],
Tian, Y.J.[Ying-Jie],
Support vector machine classifier with truncated pinball loss,
PR(68), No. 1, 2017, pp. 199-210.
Elsevier DOI
1704
Pinball loss
BibRef
Zhao, J.W.[Jin-Wei],
Yan, G.R.[Gui-Rong],
Feng, B.Q.[Bo-Qin],
Mao, W.T.[Wen-Tao],
Bai, J.Q.[Jun-Qing],
An adaptive support vector regression based on a new sequence of
unified orthogonal polynomials,
PR(46), No. 3, March 2013, pp. 899-913.
Elsevier DOI
1212
Chebyshev polynomials; Kernel function; Adaptable measures; Small
sample; Generalization ability
BibRef
Ji, Y.[You],
Sun, S.L.[Shi-Liang],
Multitask multiclass support vector machines: Model and experiments,
PR(46), No. 3, March 2013, pp. 914-924.
Elsevier DOI
1212
Multiclass classification; Multitask learning; Support vector machine;
Kernel; Regularization
BibRef
Peng, J.X.[Jian-Xun],
Ferguson, S.[Stuart],
Rafferty, K.[Karen],
Stewart, V.[Victoria],
A sequential algorithm for sparse support vector classifiers,
PR(46), No. 4, April 2013, pp. 1195-1208.
Elsevier DOI
1301
Support vector classifier; Sequential algorithm; Sparse design
BibRef
Wang, Z.[Zhen],
Shao, Y.H.[Yuan-Hai],
Wu, T.R.[Tie-Ru],
A GA-based model selection for smooth twin parametric-margin support
vector machine,
PR(46), No. 8, August 2013, pp. 2267-2277.
Elsevier DOI
1304
Pattern classification; Support vector machine; Twin support vector
machine; Smoothing techniques; Genetic algorithm
BibRef
Souza, R.C.S.N.P.[Roberto C.S.N.P.],
Leite, S.C.[Saul C.],
Borges, C.C.H.[Carlos C.H.],
Neto, R.F.[Raul Fonseca],
Online algorithm based on support vectors for orthogonal regression,
PRL(34), No. 12, 1 September 2013, pp. 1394-1404.
Elsevier DOI
1306
Support vector machines; Online algorithms; Kernel methods;
Regression problem; Orthogonal regression
BibRef
Hou, C.P.[Chen-Ping],
Nie, F.P.[Fei-Ping],
Zhang, C.S.[Chang-Shui],
Yi, D.Y.[Dong-Yun],
Wu, Y.[Yi],
Multiple rank multi-linear SVM for matrix data classification,
PR(47), No. 1, 2014, pp. 454-469.
Elsevier DOI
1310
Pattern recognition
BibRef
Kim, K.[Kyoungok],
Lee, D.W.[Dae-Won],
Inductive manifold learning using structured support vector machine,
PR(47), No. 1, 2014, pp. 470-479.
Elsevier DOI
1310
Dimensionality reduction
BibRef
Aytar, Y.[Yusuf],
Zisserman, A.[Andrew],
Part level transfer regularization for enhancing exemplar SVMs,
CVIU(138), No. 1, 2015, pp. 114-123.
Elsevier DOI
1506
BibRef
Earlier:
Enhancing Exemplar SVMs using Part Level Transfer Regularization,
BMVC12(79).
DOI Link
1301
BibRef
And:
Multi-Task Multi-Sample Learning,
TASKCV14(78-91).
Springer DOI
1504
Exemplar SVMs
BibRef
Wang, D.[Di],
Zhang, X.Q.[Xiao-Qin],
Fan, M.Y.[Ming-Yu],
Ye, X.Z.[Xiu-Zi],
Hierarchical mixing linear support vector machines for nonlinear
classification,
PR(59), No. 1, 2016, pp. 255-267.
Elsevier DOI
1609
Support vector machine
BibRef
Zhu, X.Q.[Xin-Qi],
Gao, Z.H.[Zheng-Hong],
An efficient gradient-based model selection algorithm for
multi-output least-squares support vector regression machines,
PRL(111), 2018, pp. 16-22.
Elsevier DOI
1808
Support vector machines, Multi-output regression,
Model selection, Leave-one-out cross-validation, Gradient descent optimization
BibRef
Shapovalova, N.[Nataliya],
Mori, G.[Greg],
Clustered Exemplar-SVM:
Discovering sub-categories for visual recognition,
ICIP15(93-97)
IEEE DOI
1512
sub-categories; visual recognition
BibRef
Xie, W.Y.[Wei-Yi],
Uhlmann, S.[Stefan],
Kiranyaz, S.[Serkan],
Gabbouj, M.[Moncef],
Incremental Learning with Support Vector Data Description,
ICPR14(3904-3909)
IEEE DOI
1412
Accuracy
BibRef
Rosales-Pérez, A.[Alejandro],
Gonzalez, J.A.[Jesus A.],
Coello-Coello, C.A.[Carlos A.],
Reyes-Garcia, C.A.[Carlos A.],
Escalante, H.J.[Hugo Jair],
Evolutionary Multi-Objective Approach for Prototype Generation and
Feature Selection,
CIARP14(424-431).
Springer DOI
1411
BibRef
Earlier: A1, A5, A2, A4, Only:
Bias and Variance Multi-objective Optimization for Support Vector
Machines Model Selection,
IbPRIA13(108-116).
Springer DOI
1307
BibRef
Fefilatyev, S.[Sergiy],
Shreve, M.[Matthew],
Kramer, K.[Kurt],
Hall, L.O.[Lawrence O.],
Goldgof, D.B.[Dmitry B.],
Kasturi, R.[Rangachar],
Daly, K.[Kendra],
Remsen, A.[Andrew],
Bunke, H.[Horst],
Label-noise reduction with support vector machines,
ICPR12(3504-3508).
WWW Link.
1302
BibRef
Huang, D.[Dong],
Lai, J.H.[Jian-Huang],
Wang, C.D.[Chang-Dong],
Incremental support vector clustering with outlier detection,
ICPR12(2339-2342).
WWW Link.
1302
BibRef
Zhang, W.Y.[Wei-Yu],
Yu, S.X.[Stella X.],
Teng, S.H.[Shang-Hua],
Power SVM: Generalization with exemplar classification uncertainty,
CVPR12(2144-2151).
IEEE DOI
1208
BibRef
Han, X.F.[Xu-Feng],
Berg, A.C.[Alexander C.],
DCMSVM:
Distributed parallel training for single-machine multiclass classifiers,
CVPR12(3554-3561).
IEEE DOI
1208
BibRef
Vedaldi, A.[Andrea],
Blaschko, M.B.[Matthew B.],
Zisserman, A.[Andrew],
Learning equivariant structured output SVM regressors,
ICCV11(959-966).
IEEE DOI
1201
BibRef
Zhang, L.H.[Li-He],
Zhang, K.Y.[Kun-Yu],
Dong, X.L.[Xiao-Li],
Online sparse learning utilizing multi-feature combination for image
classification,
ICIP11(197-200).
IEEE DOI
1201
BibRef
Liu, X.B.[Xiao-Bai],
Yuan, X.T.[Xiao-Tong],
Yan, S.C.[Shui-Cheng],
Jin, H.[Hai],
Multi-class semi-supervised SVMs with Positiveness Exclusive
Regularization,
ICCV11(1435-1442).
IEEE DOI
1201
BibRef
Kapp, M.N.[Marcelo N.],
Sabourin, R.[Robert],
Maupin, P.[Patrick],
Adaptive Incremental Learning with an Ensemble of Support Vector
Machines,
ICPR10(4048-4051).
IEEE DOI
1008
BibRef
Díaz-Chito, K.[Katerine],
Ferri, F.J.[Francesc J.],
Díaz-Villanueva, W.[Wladimiro],
Null Space Based Image Recognition Using Incremental Eigendecomposition,
IbPRIA11(313-320).
Springer DOI
1106
BibRef
Earlier:
Image Recognition through Incremental Discriminative Common Vectors,
ACIVS10(II: 304-311).
Springer DOI
1012
BibRef
And:
An Empirical Evaluation of Common Vector Based Classification Methods
and Some Extensions,
SSPR08(977-985).
Springer DOI
0812
BibRef
Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Support Vector Machines, SVM, Applied to Recognition .