14.2.18.4 Support Vector Machines, SVM, Feature Selection

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

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

Shashua, A.[Amnon], Wolf, L.B.[Lior B.],
Kernel Feature Selection with Side Data Using a Spectral Approach,
ECCV04(Vol III: 39-53).
Springer DOI 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 Link. 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 Link. 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 Link. 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 0211
BibRef

Haasdonk, B.[Bernard], Bahlmann, C.[Claus],
Learning with Distance Substitution Kernels,
DAGM04(220-227).
Springer DOI 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 Link. 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 0801
BibRef

Wang, L.[Lei],
Feature Selection with Kernel Class Separability,
PAMI(30), No. 9, September 2008, pp. 1534-1546.
IEEE DOI 0808
BibRef
Earlier:
Feature Subset Selection for Multi-class SVM Based Image Classification,
ACCV07(II: 145-154).
Springer DOI 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 DOI 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 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 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 DOI 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 0906
See also Kernel-Based Domain-Invariant Feature Selection in Hyperspectral Images for Transfer Learning. 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 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 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 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 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 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 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 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 Link. 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 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 1103
BibRef

Wang, R.[Ran], Kwong, S.[Sam], Chen, D.[Degang],
Inconsistency-based active learning for support vector machines,
PR(45), No. 10, October 2012, pp. 3751-3767.
Elsevier DOI 1206
Active learning; Concept learning; Inconsistency; Sample selection; Support vector machine BibRef

Wang, R.[Ran], Kwong, S.[Sam],
Active learning with multi-criteria decision making systems,
PR(47), No. 9, 2014, pp. 3106-3119.
Elsevier DOI 1406
Active learning BibRef

Tao, J.W.[Jian-Wen], Chung, F.L.[Fu-Lai], Wang, S.T.[Shi-Tong],
On minimum distribution discrepancy support vector machine for domain adaptation,
PR(45), No. 11, November 2012, pp. 3962-3984.
Elsevier DOI 1206
Domain adaptation learning; Support vector machine; Pattern classification; Maximum mean discrepancy; Maximum scatter discrepancy BibRef

Liu, D.H.[De-Hua], Qian, H.[Hui], Dai, G.[Guang], Zhang, Z.H.[Zhi-Hua],
An iterative SVM approach to feature selection and classification in high-dimensional datasets,
PR(46), No. 9, September 2013, pp. 2531-2537.
Elsevier DOI 1305
Feature selection; SVM; DrSVM; Sparse learning BibRef

Patanè, G.[Giuseppe],
Multi-resolutive sparse approximations of d-dimensional data,
CVIU(117), No. 4, April 2013, pp. 418-428.
Elsevier DOI 1303
Sparse approximation; Support Vector Machine; Image analysis; Least-squares approximation; Reproducing Kernel Hilbert Space; Radial basis functions; Spectral graph theory; Manifold learning BibRef

Liang, X.[Xun], Ma, Y.[Yuefeng], He, Y.[Yangbo], Yu, L.[Li], Chen, R.C.[Rong-Chang], Liu, T.[Tao], Yang, X.P.[Xiao-Ping], Chen, T.S.[Tung-Shou],
Fast pruning superfluous support vectors in SVMs,
PRL(34), No. 10, 15 July 2013, pp. 1203-1209.
Elsevier DOI 1306
Superfluous support vectors; Collinear support vectors; Parallel support vectors; Fast pruning; Decision function; Support vector machine BibRef

Pedergnana, M., Marpu, P.R., Mura, M.D., Benediktsson, J.A., Bruzzone, L.,
A Novel Technique for Optimal Feature Selection in Attribute Profiles Based on Genetic Algorithms,
GeoRS(51), No. 6, 2013, pp. 3514-3528.
IEEE DOI feature rank; genetic algorithms; remote sensing scene; support vector machines (SVMs) 1307
BibRef

Tayal, A.[Aditya], Coleman, T.F.[Thomas F.], Li, Y.Y.[Yu-Ying],
Primal explicit max margin feature selection for nonlinear support vector machines,
PR(47), No. 6, 2014, pp. 2153-2164.
Elsevier DOI 1403
Feature selection BibRef

Krell, M.M.[Mario Michael], Feess, D., Straube, S.,
Balanced Relative Margin Machine: The missing piece between FDA and SVM classification,
PRL(41), No. 1, 2014, pp. 43-52.
Elsevier DOI 1403
Support vector machines BibRef

Ghamisi, P., Couceiro, M.S., Benediktsson, J.A.,
A Novel Feature Selection Approach Based on FODPSO and SVM,
GeoRS(53), No. 5, May 2015, pp. 2935-2947.
IEEE DOI 1502
data reduction BibRef

Ratto, C.R., Caceres, C.A., Schoeberlein, H.C.,
Cost-Constrained Feature Optimization in Kernel Machine Classifiers,
SPLetters(22), No. 12, December 2015, pp. 2469-2473.
IEEE DOI 1512
feature extraction BibRef

Spetale, F.E.[Flavio E.], Bulacio, P.[Pilar], Guillaume, S.[Serge], Murillo, J.[Javier], Tapia, E.[Elizabeth],
A spectral envelope approach towards effective SVM-RFE on infrared data,
PRL(71), No. 1, 2016, pp. 59-65.
Elsevier DOI 1602
Spectral envelope SVM-RFE: Support Vector Machine Recursive Feature Elimination. BibRef

Paul, S.[Saurabh], Magdon-Ismail, M.[Malik], Drineas, P.[Petros],
Feature selection for linear SVM with provable guarantees,
PR(60), No. 1, 2016, pp. 205-214.
Elsevier DOI 1609
Feature Selection BibRef


Zhang, J.[Jing], Hu, X.G.[Xue-Gang], Li, P.P.[Pei-Pei], He, W.[Wei], Zhang, Y.H.[Yu-Hong], Li, H.Z.[Hui-Zong],
A Hybrid Feature Selection Approach by Correlation-Based Filters and SVM-RFE,
ICPR14(3684-3689)
IEEE DOI 1412
Accuracy BibRef

Imani, M., Ghassemian, H.,
The Investigation of Sensitivity of SVM Classifier Respect to the Number of Fetures and the Number of Training Samples,
SMPR13(209-214).
HTML Version. 1311
BibRef

Rzeniewicz, J.[Jacek], Szymanski, J.[Julian],
Selecting Features with SVM,
CIARP13(I:319-325).
Springer DOI 1311
BibRef

Maldonado, S.[Sebastián], Weber, R.[Richard],
Embedded Feature Selection for Support Vector Machines: State-of-the-Art and Future Challenges,
CIARP11(304-311).
Springer DOI 1111
BibRef

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

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

Moon, S.[Sangwoo], Qi, H.R.[Hai-Rong],
Effective Dimensionality Reduction Based on Support Vector Machine,
ICPR10(173-176).
IEEE DOI 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 1007
BibRef

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

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

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

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


Last update:Nov 11, 2017 at 13:31:57