Barzilay, O.[Ofir],
Brailovsky, V.L.,
On domain knowledge and feature selection using a support vector
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BibRef
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Wolf, L.B.[Lior B.],
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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
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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.
Elsevier DOI
0407
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Kumar, R.,
Kulkarni, A.,
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Symbolization assisted SVM classifier for noisy data,
PRL(25), No. 4, March 2004, pp. 495-504.
Elsevier DOI
0402
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Kumar, R.,
Jayaraman, V.K.,
Kulkarni, B.D.,
An SVM classifier incorporating simultaneous noise reduction and
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PR(38), No. 1, January 2005, pp. 41-49.
Elsevier DOI
0410
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Haasdonk, B.[Bernard],
Feature Space Interpretation of SVMs with Indefinite Kernels,
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Learning with Distance Substitution Kernels,
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0505
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Elsevier DOI
0711
Support vector clustering; Cluster validity measure;
Parameter learning; Parameter selection
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Wang, J.S.[Jeen-Shing],
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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.
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Wang, L.[Lei],
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Image retrieval with SVM active learning embedding Euclidean search,
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0312
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Wang, L.[Lei],
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Incorporating prior knowledge into SVM for image retrieval,
ICPR04(II: 981-984).
IEEE DOI
0409
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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
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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
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
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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.F.[Yue-Feng],
He, Y.B.[Yang-Bo],
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.,
Dalla Mura, M.,
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
Shao, Y.H.[Yuan-Hai],
Li, C.N.[Chun-Na],
Liu, M.Z.[Ming-Zeng],
Wang, Z.[Zhen],
Deng, N.Y.[Nai-Yang],
Sparse Lq-norm least squares support vector machine with feature
selection,
PR(78), 2018, pp. 167-181.
Elsevier DOI
1804
Least squares support vector machine (LS-SVM), -norm,
Feature selection, Sparse approximation, Global optimality
BibRef
Artemiou, A.[Andreas],
Dong, Y.X.[Yue-Xiao],
Shin, S.J.[Seung Jun],
Real-time sufficient dimension reduction through principal least
squares support vector machines,
PR(112), 2021, pp. 107768.
Elsevier DOI
2102
Central subspace, Ladle estimator,
Online sliced inverse regression, Streamed data
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Guo, Y.N.[Yi-Nan],
Zhang, Z.R.[Zi-Rui],
Tang, F.Z.[Feng-Zhen],
Feature selection with kernelized multi-class support vector machine,
PR(117), 2021, pp. 107988.
Elsevier DOI
2106
Feature selection, Multi-class support vector machine,
Kernel machine, Recursive feature elimination
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Rekha, K.S.[Krishnamoorthy Sashi],
Amali, S.A.M.J.[Suthanthira Amalraj Miruna Joe],
Efficient feature subset selection and classification using levy
flight-based cuckoo search optimization with parallel support vector
machine for the breast cancer data,
IJIST(32), No. 3, 2022, pp. 869-881.
DOI Link
2205
breast cancer, classification, feature selection, K-means algorithm,
parallel support vector machine
BibRef
Shang, Y.Q.[Yi-Qun],
Zheng, X.[Xinqi],
Li, J.Y.[Jia-Yang],
Liu, D.Y.[Dong-Ya],
Wang, P.P.[Pei-Pei],
A Comparative Analysis of Swarm Intelligence and Evolutionary
Algorithms for Feature Selection in SVM-Based Hyperspectral Image
Classification,
RS(14), No. 13, 2022, pp. xx-yy.
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2208
BibRef
Darvishnezhad, M.,
Ghassemian, H.,
Imani, M.,
Local Binary Graph Feature Reduction for Three-dimensional Gabor Filter
Based Hyperspectral Image Classification,
SMPR19(285-291).
DOI Link
1912
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
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Rzeniewicz, J.[Jacek],
Szymanski, J.[Julian],
Selecting Features with SVM,
CIARP13(I:319-325).
Springer DOI
1311
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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
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Bravo, C.[Cristián],
Weber, R.[Richard],
Semi-supervised Constrained Clustering with Cluster Outlier Filtering,
CIARP11(347-354).
Springer DOI
1111
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Luckner, M.[Marcin],
Reducing Number of Classifiers in DAGSVM Based on Class Similarity,
CIAP11(I: 514-523).
Springer DOI
1109
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Moon, S.[Sangwoo],
Qi, H.R.[Hai-Rong],
Effective Dimensionality Reduction Based on Support Vector Machine,
ICPR10(173-176).
IEEE DOI
1008
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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
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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 .