A new model selection method for SVM

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 4224 LNCS, Issue , 2006, Pages 99-107

  Lebrun, G ^a   Lezoray, O ^a   Charrier, C ^a   Cardot, H ^b  

^a Universite de Caen Basse Normandie   (France)

^b UNIVERSITÉ DE TOURS   (France)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATIONAL COMPLEXITY; DECISION MAKING; MATHEMATICAL MODELS; OPTIMIZATION; VECTOR QUANTIZATION;

BINARY DECISION FUNCTIONS (BDF); DECISION FUNCTION QUALITY (DFQ); SUPPORT VECTOR MACHINES (SVM); TABU SEARCH;

LEARNING SYSTEMS;

EID: 33750543943     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11875581_12     Document Type: Conference Paper

References (18)

1. Platt, J.: Fast training of SVMs using sequential minimal optimization, advances in kernel methods-support vector learning. MIT Press (1999) 185-208
2. Yu, H., Yang, J., Han, J.: Classifying large data sets using SVM with hierarchical clusters. In: SIGKDD. (2003) 306-315
3. Lebrun, G., Charrier, C., Cardot, H.: SVM training time reduction using vector quantization. In: ICPR. Volume 1. (2004) 160-163
4. Chang, C.C., Lin, C.J.: Libsvm: a library for support vector machines. Sofware Available at http://www.csie.ntu.edu.tw/~cjlin/libsvm (2001)
5. Ou, Y.Y., Chen, C.Y., Hwang, S.C., Oyang, Y.J.: Expediting model selection for SVMs based on data reduction. In: IEEE Proc. SMC. (2003) 786-791
6. Tsang, I.W., Kwok, J.T., Cheung, P.M.: Core vector machines: Fast SVM training on very large data sets. JMLR 6 (2005) 363-392
7. Lebrun, G., Charrier, C., Lezoray, O., Meurie, C., Cardot, H.: Fast pixel classification by SVM using vector quantization, tabu search and hybrid color space. In: CAIP. (2005) 685-692
8. Chapelle, O., Vapnik, V., Bousquet, O., Mukherjee, S.: Choosing multiple parameters for support vector machines. Machine Learning 46 (2002) 131-159
9. Chapelle, O., Vapnik, V.: Model selection for support vector machines. In: Advances in Neural Information Processing Systems. Volume 12. (1999) 230-236
10. Fröhlich, H., Chapelle, O., Schölkopf, B.: Feature selection for support vector machines using genetic algorithms. IJAIT 13 (2004) 791-800
11. Rifkin, R., Klautau, A.: In defense of one-vs-all classification. JMLR 5 (2004) 101-141
12. Christianini, N.: Dimension reduction in text classification with support vector machines. JMLR 6 (2005) 37-53
13. Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic (1991)
14. Staelin, C.: Parameter selection for support vector machines, http://www.hpl.hp.com/techreports/2002/HPL-2002-354R1.html (2002)
15. Glover, F., Laguna, M.: Tabu search. Kluwer Academic Publishers (1997)
16. Korycinski, D., Crawford, M.M., Barnes, J.W.: Adaptive feature selection for hyperspectral data analysis. SPIE 5238 (2004) 213-225
17. Vapnik, V.N.: Statistical Learning Theory. Wiley edn. New York (1998)
18. Blake, C., Merz, C.: Uci repository of machine learning databases, advances in kernel methods, support vector learning. (1998)