14.2.16 Support Vector Machines, SVM

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 Link 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.
WWW Version. 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 Abstract.
WWW Version. 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 Link 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 Link 0108
BibRef

Guillamet, D.[David], Vitrià, J.[Jordi],
Discriminant Local Regions Using Support Vector Machines,
ELCVIA(1), 2002, pp. None.
WWW Version. 0206
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.
WWW Version. 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 Link
WWW Version. 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 Link 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., 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.
WWW Version. 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 Link 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 Link
WWW Version. 1101
Sequential data; Clustering; Hidden Markov models BibRef

Fei, Y.N., Lu, Z., Tang, W.H., Wu, Q.H.,
Harmonic Estimation Using a Global Search Optimiser,
EvoIASP07(261-270).
Springer DOI Link 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.
WWW Version. 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.
WWW Version. 0304
BibRef

Cao, L.J.[Li Juan], Lee, H.P.[Heow Pueh], Chong, W.K.[Wai Keong],
Modified support vector novelty detector using training data with outliers,
PRL(24), No. 14, October 2003, pp. 2479-2487.
WWW Version. 0307
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.
WWW Version. 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.
WWW Version. 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 Link 0211
BibRef

Foody, G.M., Mathur, A.,
A Relative Evaluation of Multiclass Image Classification by Support Vector Machines,
GeoRS(42), No. 6, June 2004, pp. 1335-1343.
IEEE Abstract. 0407
BibRef

Foody, G.M., Mathur, A.,
Toward intelligent training of supervised image classifications: Directing training data acquisition for SVM classification,
RSE(93), No. 1-2, 2004, pp. 107-117.
Elsevier DOI Link 1102
BibRef

Zhan, Y.Q.A.[Yi-Qi-Ang], Shen, D.G.[Ding-Gang],
Design efficient support vector machine for fast classification,
PR(38), No. 1, January 2005, pp. 157-161.
WWW Version. 0410
BibRef

Zhan, Y.Q.A.[Yi-Qi-Ang], Shen, D.G.[Ding-Gang],
An adaptive error penalization method for training an efficient and generalized SVM,
PR(39), No. 3, March 2006, pp. 342-350.
WWW Version. 0601
BibRef

Lin, C.F.[Chun-Fu], Wang, S.D.[Sheng-De],
Training algorithms for fuzzy support vector machines with noisy data,
PRL(25), No. 14, 15 October 2004, pp. 1647-1656.
WWW Version. 0410
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 Link 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.
WWW Version. 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 Link
WWW Version. 1003
Large-scale problem; Kernel methods; Support vector clustering; Cluster labeling; Dynamical system BibRef

Shin, H.J.[Hyun-Jung], Cho, S.Z.[Sung-Zoon],
Invariance of neighborhood relation under input space to feature space mapping,
PRL(26), No. 6, 1 May 2005, pp. 707-718.
WWW Version. 0501
SVM training by determining which will be useful. 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.
WWW Version. 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.
WWW Version. 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.
WWW Version. 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.
WWW Version. 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.
WWW Version. 0508
BibRef

González, L., Angulo, C., Velasco, F., Català, A.,
Dual unification of bi-class support vector machine formulations,
PR(39), No. 7, July 2006, pp. 1325-1332.
WWW Version. 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.
WWW Version. 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.
WWW Version. 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 Link
WWW Version. 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).
WWW Version. 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.
WWW Version. Clustering; Support vector machines; R*-tree 0606
BibRef

Kikuchi, T.[Tomonori], Abe, S.[Shigeo],
Comparison between error correcting output codes and fuzzy support vector machines,
PRL(26), No. 12, September 2005, pp. 1937-1945.
WWW Version. 0508
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.
WWW Version. 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.
WWW Version. 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 Link 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.
WWW Version. 0606
BibRef
Earlier:
SVM-based classifier design with controlled confidence,
ICPR04(I: 164-167).
IEEE DOI Link 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.
WWW Version. 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.
WWW Version. 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 Link 0701
BibRef

Chen, Y.X.[Yi-Xin], Bi, J.B.[Jin-Bo], Wang, J.Z.[James Z.],
MILES: Multiple-Instance Learning via Embedded Instance Selection,
PAMI(28), No. 12, December 2006, pp. 1931-1947.
IEEE DOI Link 0611
BibRef
Earlier: A2, A1, A3:
A Sparse Support Vector Machine Approach to Region-Based Image Categorization,
CVPR05(I: 1121-1128).
IEEE DOI Link 0507
Training labels on sets of instances not single instances. 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 Link 0704
A binary SVM classifier that determines two nonparallel planes by solving two related SVM-type problems. BibRef

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

Jayadeva, Shah, S.[Sameena], Chandra, S.[Suresh],
Zero Norm Least Squares Proximal SVR,
PReMI09(38-43).
Springer DOI Link 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.
WWW Version. 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 Link
WWW Version. 1007
Minimum enclosing ball; Online classifier; Core vector machine; Support vector machine; Machine learning BibRef

Bazi, Y., Melgani, F.,
Toward an Optimal SVM Classification System for Hyperspectral Remote Sensing Images,
GeoRS(44), No. 11, November 2006, pp. 3374-3385.
IEEE DOI Link 0611
BibRef

Bazi, Y., Melgani, F.,
Semisupervised PSO-SVM Regression for Biophysical Parameter Estimation,
GeoRS(45), No. 6, June 2007, pp. 1887-1895.
IEEE DOI Link 0706
See also Gaussian Process Approach to Remote Sensing Image Classification. BibRef

Ghoggali, N., Melgani, F., Bazi, Y.,
A Multiobjective Genetic SVM Approach for Classification Problems With Limited Training Samples,
GeoRS(47), No. 6, June 2009, pp. 1707-1718.
IEEE DOI Link 0905
BibRef

Ghoggali, N., Melgani, F.,
Automatic Ground-Truth Validation With Genetic Algorithms for Multispectral Image Classification,
GeoRS(47), No. 7, July 2009, pp. 2172-2181.
IEEE DOI Link 0906
BibRef

Paoli, A., Melgani, F., Pasolli, E.,
Clustering of Hyperspectral Images Based on Multiobjective Particle Swarm Optimization,
GeoRS(47), No. 12, December 2009, pp. 4175-4188.
IEEE DOI Link 0912
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 Link 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 Link 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.
WWW Version. 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.
WWW Version. 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 Link 0710
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.
WWW Version. 0711
SVM; Multi-category classification; Pairwise; Uncertainty sampling BibRef

Zafeiriou, S., Tefas, A., Pitas, I.,
Minimum Class Variance Support Vector Machines,
IP(16), No. 10, October 2007, pp. 2551-2564.
IEEE DOI Link 0711
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.
WWW Version. 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 Link 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.
WWW Version. 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.
WWW Version. 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.
WWW Version. 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.
WWW Version. 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 Link
WWW Version. 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.
WWW Version. 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.
WWW Version. 0809
Outlier detection; Support vector domain description BibRef

El-Yaniv, R.[Ran], Pechyony, D.[Dmitry], Yom-Tov, E.[Elad],
Better multiclass classification via a margin-optimized single binary problem,
PRL(29), No. 14, October 2008, pp. 1954-1959.
WWW Version. 0804
Multiclass classification; Support vector machines; Multiple kernel learning 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 Link 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 Link
WWW Version. 0902
SVM; Support vector machines; Kernel parameters; Inter-cluster distances BibRef

Tang, Y., Zhang, Y.Q., Chawla, N.V., Krasser, S.,
SVMs Modeling for Highly Imbalanced Classification,
SMC-B(39), No. 1, February 2009, pp. 281-288.
IEEE DOI Link 0902
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 Link
WWW Version. 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 Link 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 Link
WWW Version. 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 Link 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 Link
WWW Version. 0906
BibRef
Earlier:
Auto-Correlation Wavelet Support Vector Machine and Its Applications to Regression,
CRV05(246-252).
IEEE DOI Link 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 Link 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 Link
WWW Version. 0907
Sparsity; Classification; Generalization error bounds; Statistical learning theory BibRef

Tuia, D., Pacifici, F.[Fabio], Kanevski, M., Emery, W.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 Link 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

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 Link 1003
BibRef

Alzate, C.[Carlos], Suykens, J.A.K.[Johan A.K.],
Multiway Spectral Clustering with Out-of-Sample Extensions through Weighted Kernel PCA,
PAMI(32), No. 2, February 2010, pp. 335-347.
IEEE DOI Link 1001
PCA approach based on SVM formulation. BibRef

Signoretto, M., van de Plas, R., de Moor, B., Suykens, J.A.K.,
Tensor Versus Matrix Completion: A Comparison With Application to Spectral Data,
SPLetters(18), No. 7, July 2011, pp. 403-406.
IEEE DOI Link 1101
BibRef

Orabona, F.[Francesco], Castellini, C.[Claudio], Caputo, B.[Barbara], Jie, L.[Luo], Sandini, G.[Giulio],
On-line independent support vector machines,
PR(43), No. 4, April 2010, pp. 1402-1412.
Elsevier DOI Link
WWW Version. 1002
BibRef
Earlier:
Indoor Place Recognition using Online Independent Support Vector Machines,
BMVC07(xx-yy).
PDF Version. 0709
Support vector machines; On-line learning; Bounded testing complexity; Linear independence BibRef

Jie, L.[Luo], Orabona, F.[Francesco], Fornoni, M.[Marco], Caputo, B.[Barbara], Cesa-Bianchi, N.[Nicolo],
OM-2: An online multi-class Multi-Kernel Learning algorithm,
OLCV10(43-50).
IEEE DOI Link 1006
BibRef

Orabona, F.[Francesco], Jie, L.[Luo], Caputo, B.[Barbara],
Online-batch strongly convex Multi Kernel Learning,
CVPR10(787-794).
IEEE DOI Link Video of talk:
WWW Version. 1006
BibRef
Earlier: A2, A1, A3:
An Online Framework for Learning Novel Concepts over Multiple Cues,
ACCV09(I: 269-280).
Springer DOI Link 0909
BibRef

Tommasi, T.[Tatiana], Orabona, F.[Francesco], Caputo, B.[Barbara],
Safety in numbers: Learning categories from few examples with multi model knowledge transfer,
CVPR10(3081-3088).
IEEE DOI Link 1006
BibRef

Jie, L.[Luo], Tommasi, T.[Tatiana], Caputo, B.[Barbara],
Multiclass transfer learning from unconstrained priors,
ICCV11(1863-1870).
IEEE DOI Link 1201
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 Link
WWW Version. 1003
BibRef
Earlier:
SVMs, Gaussian mixtures, and their generative/discriminative fusion,
ICPR08(1-4).
IEEE DOI Link 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 Version. 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 Link
WWW Version. 1004
BibRef
Earlier: A2, A1: CIARP08(332-339).
Springer DOI Link 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 Link 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 Link 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 Link
WWW Version. 1004
Support vector machine; Particle swarm optimization; Genetic algorithm; Optimization; Discriminant function; Hyperplane BibRef

Gonen, M.[Mehmet], Alpaydin, E.[Ethem],
Cost-conscious multiple kernel learning,
PRL(31), No. 9, 1 July 2010, pp. 959-965.
Elsevier DOI Link
WWW Version. 1004
Support vector machines; Kernel combination; Multiple kernel learning BibRef

Gonen, M.[Mehmet], Alpaydin, E.[Ethem],
Regularizing multiple kernel learning using response surface methodology,
PR(44), No. 1, January 2011, pp. 159-171.
Elsevier DOI Link
WWW Version. 1011
BibRef
And:
Localized Multiple Kernel Regression,
ICPR10(1425-1428).
IEEE DOI Link 1008
Support vector machine; Multiple kernel learning; Regularization; Response surface methodology 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 Link
WWW Version. 1006
Supervised learning; Support vector machine; Minimum class variance support machine; Locality preserving projections BibRef

Tommasi, T.[Tatiana], Caputo, B.[Barbara],
The more you know, the less you learn: from knowledge transfer to one-shot learning of object categories,
BMVC09(xx-yy).
PDF Version. 0909
Learning from only a few examples. BibRef

Karacali, B.[Bilge],
Quasi-supervised learning for biomedical data analysis,
PR(43), No. 10, October 2010, pp. 3674-3682.
Elsevier DOI Link
WWW Version. 1007
Biomedical data analysis; Abnormality detection; Nearest neighbor rule; Support vector machines; Flow cytometry; Electroencephalography 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 Link 1007
With few initial points. BibRef

Davenport, M.A.[Mark A.], Baraniuk, R.G.[Richard G.], Scott, C.D.[Clayton D.],
Tuning Support Vector Machines for Minimax and Neyman-Pearson Classification,
PAMI(32), No. 10, October 2010, pp. 1888-1898.
IEEE DOI Link 1008
SVM training. Neyman-Pearson usually is sensitive to errors. BibRef

Kim, J.S.[Joo-Seuk], Scott, C.D.[Clayton D.],
L(2) Kernel Classification,
PAMI(32), No. 10, October 2010, pp. 1822-1831.
IEEE DOI Link 1008
Vs. KDE and SVM approaches. Optimizes the integrated squared error (ISE) of a difference of densities. L_2 usually poor for higher dimensions (vs. SVM), regularization to improve this. 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 Link
WWW Version. 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 Link
WWW Version. 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 Link
WWW Version. 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 Link 1003
BibRef

Huang, K.[Kaizhu], Zheng, D.[Danian], 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 Link
WWW Version. 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.[Yannan],
Multi-weight vector projection support vector machines,
PRL(31), No. 13, 1 October 2010, pp. 2006-2011.
Elsevier DOI Link
WWW Version. 1003
Generalized eigenvalues; Multi-weight vector; Matrix singularity; Standard eigenvalues; Singular problems 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 Link 1011
BibRef

Maulik, U.[Ujjwal], Chakraborty, D.[Debasis],
A self-trained ensemble with semisupervised SVM: An application to pixel classification of remote sensing imagery,
PR(44), No. 3, March 2011, pp. 615-623.
Elsevier DOI Link
WWW Version. 1011
Semisupervised learning; Support vector machines; Remote sensing satellite images; Quadratic programming; Self-training; Classifier ensemble 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 Link 1101
Ramp Loss. Supress influence of outliesrs. BibRef

Han, D.Q.A.[De-Qi-Ang], 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 Link
WWW Version. 1101
Nearest feature line (NFL); Trespass inaccuracy; Feature line segment; Geometric relation; Neighborhood-based classifier BibRef

Han, D.Q.A.[De-Qi-Ang], 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 Link 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 Link
WWW Version. 1102
Confidence; Hidden classes; Multi-class classification; Smooth method; Support vector machine; Ternary voting games BibRef

Guo, L.[Lihua], Jin, L.W.[Lian-Wen],
Laplacian Support Vector Machines with Multi-Kernel Learning,
IEICE(E94-D), No. 2, February 2011, pp. 379-383.
WWW Version. 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 Link 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 Link 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 Link 1011
Award, ACCV Best Paper. 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 Link
WWW Version. 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 Link
WWW Version. 1103
Generalized Chebyshev kernel; Modified Chebyshev kernel; Semi-parametric kernel; Kernel construction BibRef

Fu, Z.Y.[Zhou-Yu], Robles-Kelly, A.[Antonio], Zhou, J.[Jun],
MILIS: Multiple Instance Learning with Instance Selection,
PAMI(33), No. 1, January 2011, pp. 958-977.
IEEE DOI Link 1104
Deals with collections of instances called bags. Each bag has instances for feature extraction. Large instance space. BibRef

Fu, Z.Y.[Zhou-Yu], Robles-Kelly, A.[Antonio],
An instance selection approach to Multiple instance Learning,
CVPR09(911-918).
IEEE DOI Link 0906
BibRef
Earlier:
Fast multiple instance learning via L1,2 logistic regression,
ICPR08(1-4).
IEEE DOI Link 0812
BibRef
And:
On Mixtures of Linear SVMs for Nonlinear Classification,
SSPR08(489-499).
Springer DOI Link 0812
BibRef

Airola, A.[Antti], Pahikkala, T.[Tapio], Salakoski, T.[Tapio],
Training linear ranking SVMs in linearithmic time using red-black trees,
PRL(32), No. 9, 1 July 2011, pp. 1328-1336.
Elsevier DOI Link
WWW Version. 1101
Bundle methods; Cutting plane method; Learning to rank; Ranking support vector machine; Red-black tree 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 Link
WWW Version. 1101
Active learning; Adaptive regularization; SVM; TSVM BibRef

Yger, F., Rakotomamonjy, A.,
Wavelet kernel learning,
PR(44), No. 10-11, October-November 2011, pp. 2614-2629.
Elsevier DOI Link
WWW Version. 1101
Wavelet; Multiple kernel learning; SVM; Quadratic mirror filter 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 Link
WWW Version. 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 Link
WWW Version. 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 Link 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 Link
WWW Version. 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 Link
WWW Version. 1112
Least square; Support vector machine; Unconstrained optimization; Conjugate gradient method 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 Link 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).
WWW Version. 1007
BibRef

Candel, D.[Diego], Ñanculef, R.[Ricardo], Concha, C.[Carlos], Allende, H.[Héctor],
A Sequential Minimal Optimization Algorithm for the All-Distances Support Vector Machine,
CIARP10(484-491).
Springer DOI Link 1011
BibRef

Frandi, E.[Emanuele], Gasparo, M.G.[Maria Grazia], Lodi, S.[Stefano], Ñanculef, R.[Ricardo], Sartori, C.[Claudio],
A New Algorithm for Training SVMs Using Approximate Minimal Enclosing Balls,
CIARP10(87-95).
Springer DOI Link 1011
BibRef

Canu, S.[Stéphane],
Recent Advances in Kernel Machines,
CIARP10(1).
Springer DOI Link 1011
SVM techniques. BibRef

Zhang, D.Y.[De-Yuan], Wang, X.L.[Xiao-Long], Liu, B.Q.[Bing-Quan],
Learning the Kernel Combination for Object Categorization,
ICPR10(2929-2932).
IEEE DOI Link 1008
Learn optimal combination of kernels before SVM training 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 Link 1008
BibRef

Wang, S.H.[Shu-Hui], Jiang, S.Q.A.[Shu-Qi-Ang], Huang, Q.M.[Qing-Ming], Tian, Q.[Qi],
Multiple Kernel Learning with High Order Kernels,
ICPR10(2138-2141).
IEEE DOI Link 1008
BibRef

Zhang, Z.[Ziming], Li, Z.N.[Ze-Nian], Drew, M.S.[Mark S.],
AdaMKL: A Novel Biconvex Multiple Kernel Learning Approach,
ICPR10(2126-2129).
IEEE DOI Link 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 Link 1008
BibRef

Guo, L.[Li], Boukir, S.[Samia], Chehata, N.[Nesrine],
Support Vectors Selection for Supervised Learning Using an Ensemble Approach,
ICPR10(37-40).
IEEE DOI Link 1008
BibRef

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

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

Lian, X.C.[Xiao-Chen], Li, Z.W.[Zhi-Wei], Lu, B.L.[Bao-Liang], Zhang, L.[Lei],
Max-Margin Dictionary Learning for Multiclass Image Categorization,
ECCV10(IV: 157-170).
Springer DOI Link 1009
for use with bag of visual terms (words) and SVM classifiers. BibRef

Wu, J.X.[Jian-Xin],
A Fast Dual Method for HIK SVM Learning,
ECCV10(II: 552-565).
Springer DOI Link 1009
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 Link 1006
BibRef

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

Demir, B.[Begm], Ertrk, S.[Sarp],
Improving SVM classification accuracy using a hierarchical approach for hyperspectral images,
ICIP09(2849-2852).
IEEE DOI Link 0911
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 Link 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 Link 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 Link 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 Link 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 Link 1009
BibRef

Gehler, P.V.[Peter V.], Nowozin, S.[Sebastian],
On Feature Combination for Multiclass Object Classification,
ICCV09(221-228).
IEEE DOI Link 0909
BibRef

Gehler, P.V.[Peter Vincent], Nowozin, S.[Sebastian],
Let the kernel figure it out; Principled learning of pre-processing for kernel classifiers,
CVPR09(2836-2843).
IEEE DOI Link 0906
BibRef

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

Fu, S.[Siyao], Guo, S.Y.[Sheng-Yang], Hou, Z.G.[Zeng-Guang], Liang, Z.Z.[Zi-Ze], Tan, M.[Min],
Multiple kernel learning from sets of partially matching image features,
ICPR08(1-4).
IEEE DOI Link 0812
SVM with multiple kernels. BibRef

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

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

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

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

Timm, F.[Fabian], Klement, S.[Sascha], Martinetz, T.[Thomas],
Fast model selection for MaxMinOver-based training of support vector machines,
ICPR08(1-4).
IEEE DOI Link 0812
BibRef

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

Sentelle, C.[Christopher], Anagnostopoulos, G.C.[Georgios C.], Georgiopoulos, M.[Michael],
A fast revised simplex method for SVM training,
ICPR08(1-4).
IEEE DOI Link 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 Link 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 Link 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 Link 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 Link 0906
BibRef

Schnitzspan, P.[Paul], Fritz, M.[Mario], Schiele, B.[Bernt],
Hierarchical Support Vector Random Fields: Joint Training to Combine Local and Global Features,
ECCV08(II: 527-540).
Springer DOI Link 0810
BibRef

Demirkesen, C.[Can], Cherifi, H.[Hocine],
A Comparison of Multiclass SVM Methods for Real World Natural Scenes,
ACIVS08(xx-yy).
Springer DOI Link 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 Link 0806
BibRef

Maji, S.[Subhransu], Berg, A.C.[Alexander C.],
Max-margin additive classifiers for detection,
ICCV09(40-47).
IEEE DOI Link 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 Link 0806
BibRef

Lucey, S.[Simon],
Enforcing non-positive weights for stable support vector tracking,
CVPR08(1-8).
IEEE DOI Link 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 Link 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 Link 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 Link 0706
BibRef

Pao, H.T.[Hsiao T.], Xu, Y.Y.[Yeong Y.], Chuang, S.C.[Shun C.], Fu, H.C.[Hsin C.],
Image Classification and Indexing by EM Based Multiple-Instance Learning,
Visual07(146-153).
Springer DOI Link 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 Link 0909
BibRef

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

Hernández, N.[Noslen], Biscay, R.J.[Rolando J.], Talavera, I.[Isneri],
Support Vector Regression Methods for Functional Data,
CIARP07(564-573).
Springer DOI Link 0711
BibRef

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

Mejía-Guevara, I.[Iván], Kuri-Morales, Á.[Ángel],
MP-Polynomial Kernel for Training Support Vector Machines,
CIARP07(584-593).
Springer DOI Link 0711
BibRef

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

Tanaka, A.[Akira], Imai, H.[Hideyuki], Kudo, M.[Mineichi], Miyakoshi, M.[Masaaki],
Optimal Kernel in a Class of Kernels with an Invariant Metric,
SSPR08(530-539).
Springer DOI Link 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 Link 0608
BibRef

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

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

Tzotsos, A.,
A support vector machine approach for object based image analysis,
OBIA06(xx-yy).
PDF Version. 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).
WWW Version. 0609
Extend SVM to enable rejection of out of class. BibRef

Dmitry, K.[Kropotov], Nikita, P.[Ptashko], Oleg, V.[Vasiliev], Dmitry, V.[Vetrov],
On Kernel Selection in Relevance Vector Machines Using Stability Principle,
ICPR06(IV: 233-236).
WWW Version. 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).
WWW Version. 0609
BibRef

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

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

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

Brun, A.[Anders], Westin, C.F.[Carl-Fredrik], Herberthson, M.[Magnus], Knutsson, H.[Hans],
Fast Manifold Learning Based on Riemannian Normal Coordinates,
SCIA05(920-929).
Springer DOI Link 0506
BibRef

Zhan, Y.Q.A.[Yi-Qi-Ang], Shen, D.G.[Ding-Gang],
Increasing Efficiency of SVM by Adaptively Penalizing Outliers,
EMMCVPR05(539-551).
Springer DOI Link 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 Link 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 Link 0509
BibRef

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

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

Boughorbel, S., Tarel, J.P., Boujemaa, N.,
Generalized Histogram Intersection Kernel for Image Recognition,
ICIP05(III: 161-164).
IEEE DOI Link 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 Link 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 Link 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 Link 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).
WWW Version. 0505
BibRef

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

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

Lebrun, G., Charrier, C., Cardot, H.,
SVM training time reduction using vector quantization,
ICPR04(I: 160-163).
IEEE DOI Link 0409
BibRef

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

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

Zhang, P.[Peng], Peng, J.[Jing],
Efficient Regularized Least Squares Classification,
LCV04(98).
IEEE DOI Link 0406
BibRef
And:
SVM vs regularized least squares classification,
ICPR04(I: 176-179).
IEEE DOI Link 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 Abstract. 0310
BibRef

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

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

Franc, V., Hlavac, V.,
Multi-class support vector machine,
ICPR02(II: 236-239).
IEEE DOI Link 0211
BibRef

Zhang, J., Zhang, Y., Zhou, T.,
Classification of Hyperspectral Data Using Support Vector Machine,
ICIP01(I: 882-885).
IEEE Abstract. 0108
BibRef

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

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

Yang, M.H.[Ming-Hsuan], Ahuja, N.[Narendra],
A Geometric Approach to Train Support Vector Machines,
CVPR00(I: 430-437).
IEEE Abstract.
WWW Version. 0005
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 Link 9410
BibRef

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

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