14.2.16.3 Support Vector Machines, SVM, Feature Selection

Barzilay, O.[Ofir], Brailovsky, V.L.,
On domain knowledge and feature selection using a support vector machine,
PRL(20), No. 5, May 1999, pp. 475-484. BibRef 9905

Wolf, L.[Lior], Shashua, A.[Amnon],
Learning over sets using kernel principal angles,
MachLearnRes(4), 2003, pp. 913-931.
WWW Version. BibRef 0300
Earlier:
Feature selection for unsupervised and supervised inference: The emergence of sparsity in a weighted-based approach,
ICCV03(378-384).
IEEE DOI Link 0311
BibRef
And:
Kernel principal angles for classification machines with applications to image sequence interpretation,
CVPR03(I: 635-640).
IEEE Abstract. 0307
BibRef

Wolf, L., Shashua, A., Mukherjee, S.,
Gene Selection via a Spectral Approach,
BioInfo05(III: 140-140).
IEEE DOI Link 0507
BibRef

Shashua, A.[Amnon], Wolf, L.[Lior],
Kernel Feature Selection with Side Data Using a Spectral Approach,
ECCV04(Vol III: 39-53).
WWW Version. 0405
BibRef

Shima, K., Todoriki, M., Suzuki, A.,
SVM-based feature selection of latent semantic features,
PRL(25), No. 9, 2 July 2004, pp. 1051-1057.
WWW Version. 0407
BibRef

Kumar, R., Kulkarni, A., Jayaraman, V.K., Kulkarni, B.D.,
Symbolization assisted SVM classifier for noisy data,
PRL(25), No. 4, March 2004, pp. 495-504.
WWW Version. 0402
BibRef

Kumar, R., Jayaraman, V.K., Kulkarni, B.D.,
An SVM classifier incorporating simultaneous noise reduction and feature selection: Illustrative case examples,
PR(38), No. 1, January 2005, pp. 41-49.
WWW Version. 0410
BibRef

Haasdonk, B.[Bernard],
Feature Space Interpretation of SVMs with Indefinite Kernels,
PAMI(27), No. 4, April 2005, pp. 482-492.
IEEE Abstract. 0501
BibRef

Haasdonk, B., Keysers, D.,
Tangent distance kernels for support vector machines,
ICPR02(II: 864-868).
IEEE DOI Link 0211
BibRef

Haasdonk, B.[Bernard], Bahlmann, C.[Claus],
Learning with Distance Substitution Kernels,
DAGM04(220-227).
WWW Version. 0505
BibRef

Wang, J.S.[Jeen-Shing], Chiang, J.C.[Jen-Chieh],
A cluster validity measure with a hybrid parameter search method for the support vector clustering algorithm,
PR(41), No. 2, February 2008, pp. 506-520.
WWW Version. 0711
Support vector clustering; Cluster validity measure; Parameter learning; Parameter selection BibRef

Wang, J.S.[Jeen-Shing], Chiang, J.C.[Jen-Chieh],
A Cluster Validity Measure With Outlier Detection for Support Vector Clustering,
SMC-B(38), No. 1, February 2007, pp. 78-89.
IEEE DOI Link 0801
BibRef

Wang, L.[Lei],
Feature Selection with Kernel Class Separability,
PAMI(30), No. 9, September 2008, pp. 1534-1546.
IEEE DOI Link 0808
BibRef
Earlier:
Feature Subset Selection for Multi-class SVM Based Image Classification,
ACCV07(II: 145-154).
Springer DOI Link 0711
See also Texture classification using multiresolution Markov random field models. BibRef

Wang, L.[Lei], Chan, K.L.[Kap Luk], Tan, Y.P.[Yap Peng],
Image retrieval with SVM active learning embedding Euclidean search,
ICIP03(I: 725-728).
IEEE Abstract. 0312
BibRef

Wang, L.[Lei], Xue, P.[Ping], Chan, K.L.[Kap Luk],
Incorporating prior knowledge into SVM for image retrieval,
ICPR04(II: 981-984).
IEEE DOI Link 0409
BibRef

Li, X.C.[Xa-Chan], Wang, L.[Lei], Sang, E.[Eric],
Multi-label SVM active learning for image classification,
ICIP04(IV: 2207-2210).
IEEE DOI Link 0505
BibRef

Wang, L.[Lei], Chan, K.L.[Kap Luk], Zhang, Z.H.[Zhi-Hua],
Bootstrapping SVM active learning by incorporating unlabelled images for image retrieval,
CVPR03(I: 629-634).
IEEE Abstract. 0307
BibRef

Bruzzone, L., Persello, C.,
A Novel Context-Sensitive Semisupervised SVM Classifier Robust to Mislabeled Training Samples,
GeoRS(47), No. 7, July 2009, pp. 2142-2154.
IEEE DOI Link 0906
BibRef

Bruzzone, L.[Lorenzo], Persello, C.,
A Novel Approach to the Selection of Spatially Invariant Features for the Classification of Hyperspectral Images With Improved Generalization Capability,
GeoRS(47), No. 9, September 2009, pp. 3180-3191.
IEEE DOI Link 0909
BibRef

Sun, Y.[Yi], Gonzalez Castellano, C.[Cristina], Robinson, M.[Mark], Adams, R.[Rod], Rust, A.G.[Alistair G.], Davey, N.[Neil],
Using pre and post-processing methods to improve binding site predictions,
PR(42), No. 9, September 2009, pp. 1949-1958.
Elsevier DOI Link
WWW Version. 0905
Feature selection; Tomek link; Filters; Support vector machines; Transcription factors BibRef

Ghannad-Rezaie, M.[Mostafa], Soltanian-Zadeh, H.[Hamid], Ying, H.[Hao], Dong, M.[Ming],
Selection-fusion approach for classification of datasets with missing values,
PR(43), No. 6, June 2010, pp. 2340-2350.
Elsevier DOI Link
WWW Version. 1003
Missing value management; Subspace classifiers; Ensemble classifiers; Multiple imputations; Pruning; Support vector machine (SVM) BibRef

Waske, B., van der Linden, S., Benediktsson, J.A., Rabe, A., Hostert, P.,
Sensitivity of Support Vector Machines to Random Feature Selection in Classification of Hyperspectral Data,
GeoRS(48), No. 7, July 2010, pp. 2880-2889.
IEEE DOI Link 1007
BibRef

Nguyen, M.H.[Minh Hoai], de la Torre, F.[Fernando],
Optimal feature selection for support vector machines,
PR(43), No. 3, March 2010, pp. 584-591.
Elsevier DOI Link
WWW Version. 1001
Support vector machine; Feature selection; Feature extraction BibRef

Pal, M., Foody, G.M.,
Feature Selection for Classification of Hyperspectral Data by SVM,
GeoRS(48), No. 5, May 2010, pp. 2297-2307.
IEEE DOI Link 1006
BibRef

Chang, C.Y.[Chuan-Yu], Chen, S.J.[Shao-Jer], Tsai, M.F.[Ming-Fong],
Application of support-vector-machine-based method for feature selection and classification of thyroid nodules in ultrasound images,
PR(43), No. 10, October 2010, pp. 3494-3506.
Elsevier DOI Link
WWW Version. 1007
Support vector machines; Feature selection; Thyroid nodule classification BibRef

Yang, X.[Xu], Xiong, H.L.[Hui-Lin], Yang, X.[Xin],
Optimal Gaussian Kernel Parameter Selection for SVM Classifier,
IEICE(E93-D), No. 12, December 2010, pp. 3352-3358.
WWW Version. 1101
BibRef

Moustakidis, S.P., Theocharis, J.B.,
SVM-FuzCoC: A novel SVM-based feature selection method using a fuzzy complementary criterion,
PR(43), No. 11, November 2010, pp. 3712-3729.
Elsevier DOI Link
WWW Version. 1008
Feature selection; Fuzzy sets; Feature redundancy; Fuzzy complementary criterion; Support vector machines BibRef

Varewyck, M., Martens, J.P.,
A Practical Approach to Model Selection for Support Vector Machines With a Gaussian Kernel,
SMC-B(41), No. 2, April 2011, pp. 330-340.
IEEE DOI Link 1103
BibRef

Bravo, C.[Cristián], Weber, R.[Richard],
Semi-supervised Constrained Clustering with Cluster Outlier Filtering,
CIARP11(347-354).
Springer DOI Link 1111
BibRef

Luckner, M.[Marcin],
Reducing Number of Classifiers in DAGSVM Based on Class Similarity,
CIAP11(I: 514-523).
Springer DOI Link 1109
BibRef

Moon, S.[Sangwoo], Qi, H.R.[Hai-Rong],
Effective Dimensionality Reduction Based on Support Vector Machine,
ICPR10(173-176).
IEEE DOI Link 1008
BibRef

Ruan, S.[Su], Zhang, N.[Nan], Lebonvallet, S.[Stephane], Liao, Q.M.[Qing-Ming], Zhu, Y.M.[Yue-Min],
Fusion and classification of multi-source images by SVM with selected features in a kernel space,
IPTA10(17-20).
IEEE DOI Link 1007
BibRef

Liang, Z.Z.[Zhi-Zheng], Zhao, T.[Tuo],
Feature selection for linear support vector machines,
ICPR06(II: 606-609).
IEEE DOI Link 0609
BibRef

Fan, Z.G.[Zhi-Gang], Lu, B.L.[Bao-Liang],
Fast Recognition of Multi-View Faces with Feature Selection,
ICCV05(I: 76-81).
IEEE DOI Link 0510
SVM based face recognition. BibRef

Neumann, J.[Julia], Schnörr, C.[Christoph], Steidl, G.[Gabriele],
SVM-Based Feature Selection by Direct Objective Minimisation,
DAGM04(212-219).
WWW Version. 0505
BibRef

Hermes, L., Buhmann, J.M.,
Feature Selection for Support Vector Machines,
ICPR00(Vol II: 712-715).
IEEE DOI Link 0009
BibRef

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