World Scientific
Skip main navigation

Cookies Notification

We use cookies on this site to enhance your user experience. By continuing to browse the site, you consent to the use of our cookies. Learn More
×
Our website is made possible by displaying certain online content using javascript.
In order to view the full content, please disable your ad blocker or whitelist our website www.worldscientific.com.

System Upgrade on Mon, Jun 21st, 2021 at 1am (EDT)

During this period, the E-commerce and registration of new users may not be available for up to 6 hours.
For online purchase, please visit us again. Contact us at [email protected] for any enquiries.

TABU SEARCH MODEL SELECTION FOR SVM

    A model selection method based on tabu search is proposed to build support vector machines (binary decision functions) of reduced complexity and efficient generalization. The aim is to build a fast and efficient support vector machines classifier. A criterion is defined to evaluate the decision function quality which blends recognition rate and the complexity of a binary decision functions together. The selection of the simplification level by vector quantization, of a feature subset and of support vector machines hyperparameters are performed by tabu search method to optimize the defined decision function quality criterion in order to find a good sub-optimal model on tractable times.

    References

    • V. N.   Vapnik , Statistical Learning Theory ( Wiley edition , New York , 1998 ) . Google Scholar
    • N.   Cristianini and J.   Shawe-Teylor , An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods ( Cambridge University Press , 2000 ) . CrossrefGoogle Scholar
    • J. Platt, Fast training of SVMs using sequential minimal optimization, advances in kernel methods-support vector learning, MIT Press (1999) 185–208 . Google Scholar
    • H. Yu, J. Yang and J. Han, Classifying large data sets using SVM with hierarchical clusters, SIGKDD (2003) pp. 306–315. Google Scholar
    • G. Lebrun, C. Charrier and H. Cardot, SVM training time reduction using vector quantization, ICPR1 (2004) pp. 160–163. Google Scholar
    • I. Steinwart, Sparseness of support vector machines - some asymptotically sharp bounds, NIPS (2004) pp. 169–184. Google Scholar
    • S. S. Keerthi, O. Chapelle and D. DeCoste, JMLR 7, 1493 (2006). ISIGoogle Scholar
    • D. Martin, C. Fowlkes and J. Malik, TPAMI 26(5), 530 (2004). CrossrefGoogle Scholar
    • C.-C. Chang and C.-J. Lin, Libsvm: A library for support vector machines. Software Available at http://www.csie.ntu.edu.tw/~cjlin/libsvm (2001) . Google Scholar
    • S. V. N. Vishwanathan, A. J. Smola and M. N. Murty, SimpleSVM, ICML (2003) pp. 760–767. Google Scholar
    • Y. Y. Ouet al., Expediting model selection for SVMs based on data reduction, IEEE Proc. SMC (2003) pp. 786–791. Google Scholar
    • I. W. Tsang, J. T. Kwok and P.-M. Cheung, JMLR 6, 363 (2005). ISIGoogle Scholar
    • E. Parrado-Herna'ndezet al., Pattern Recognition 36(7), 1479 (2003), DOI: 10.1016/S0031-3203(02)00351-5. Crossref, ISIGoogle Scholar
    • J. Yang, Z.-W. Li and J.-P. Zhang, A training algorithm of incremental support vector machine with recombining method, Machine Learning and Cybernetics7 (2005) pp. 4285–4288. Google Scholar
    • S. Katagiri and S. Abe, Pattern Recogn. Lett. 27(13), 1495 (2006), DOI: 10.1016/j.patrec.2006.02.016. Crossref, ISIGoogle Scholar
    • R.   Herbrich , Learning Kernel Classifiers ( The MIT Press , 2002 ) . Google Scholar
    • K. Lin and C. Lin, Neural Networks 14(6), 1449 (2003). ISIGoogle Scholar
    • T. Thies and F. Weber, Neural Comput. 16(9), 1769 (2004), DOI: 10.1162/0899766041336459. Crossref, Medline, ISIGoogle Scholar
    • S.   Abe , Support Vector Machines for Pattern Classification ( Springer , 2005 ) . Google Scholar
    • N. Christianini, JMLR 6, 37 (2005). ISIGoogle Scholar
    • O. Chapelleet al., Machine Learning 46(1–3), 131 (2002), DOI: 10.1023/A:1012450327387. Crossref, ISIGoogle Scholar
    • H. Fröhlich, O. Chapelle and B. Schölkopf, International Journal on Artificial Intelligence Tools 13(4), 791 (2004). LinkGoogle Scholar
    • J. Bi, Multi-objective programming in SVMs, ICML (2003) pp. 35–42. Google Scholar
    • O. Chapelle and V. Vapnik, Advances in Neural Information Processing Systems 12 (1999) pp. 230–236. Google Scholar
    • H. Nakayamaet al., European Journal of Operational Research 166(3), 756 (2005), DOI: 10.1016/j.ejor.2004.03.043. Crossref, ISIGoogle Scholar
    • R. Rifkin and A. Klautau, JMLR 5, 101 (2004). ISIGoogle Scholar
    • G.   Lebrun et al. , Encyclopedia of Artificial Intelligence , Information Science Reference , eds. J. R.   Rabunal , J.   Dorado and A.   Pazos ( 2008 ) . Google Scholar
    • G. Lebrunet al., Fast pixel classification by SVM using vector quantization, tabu search and hybrid color space, CAIP (2005) pp. 685–692. Google Scholar
    • G. Lebrunet al., A new model selection method for SVM, IDEAL (2006) pp. 99–107. Google Scholar
    • G. Lebrunet al., Cellular and Molecular Biology, Special Issue on Signal and Image Processing 53(2), 51 (2007). Google Scholar
    • A.   Tikhonov and V.   Arsenin , Solution of Ill-Posed Problems ( Winston & Sons , 1977 ) . Google Scholar
    • A.   Tikhonov and V.   Arsenin , Ill-Posed Problems: Theory and Applications ( Kluwer Academic Publishers , 1994 ) . Google Scholar
    • C. A. C.   Coello , G. B.   Lamont and D. A. V.   Veldhuizen , Evolutionary Algorithms for Solving Multi-Objective Problems   5 ( Kluwer Academic , 2002 ) . CrossrefGoogle Scholar
    • A. P.   Engelbrecht , Fundamentals of Computational Swarm Intelligence ( Wiley , 2006 ) . Google Scholar
    • A.   Gersho and R. M.   Gray , Vector Quantization and Signal Compression ( Kluwer Academic , 1991 ) . Google Scholar
    • J.   Han and M.   Kamber , Data Mining: Concepts and Techniques , The Morgan Kaufmann Series in Data Management Systems, Jim Gray, Series Editor ( Morgan Kaufmann Publishers , 2000 ) . Google Scholar
    • T.   Acharya and S.   Mitra , Data mining: multimedia, Soft Computing and Bioinformatics ( John Wiley and Sons , 2003 ) . Google Scholar
    • P. N.   Tan , M.   Steinbach and V.   Kumar , Introduction to Data Mining ( Addison-Wesley , 2006 ) . Google Scholar
    • J.-X. Dong, A. Krzyzak and C. Y. Suen, Pattern Recognition Letters 26(12), 1849 (2005), DOI: 10.1016/j.patrec.2005.03.006. Crossref, ISIGoogle Scholar
    • F.   Glover and M.   Laguna , Tabu Search ( Kluwer Academic Publishers , 1997 ) . CrossrefGoogle Scholar
    • D. Korycinski, M. M. Crawford and J. W. Barnes, SPIE 5238, 213 (2004), DOI: 10.1117/12.517487. Google Scholar
    • Ping-Feng Pai and Yu-Ying Huang, Using directed acyclic graph support vector machines with tabu search for classifying faulty product types, ISNN (2006) pp. 1117–1125. Google Scholar
    • N. Vandenbroucke, L. Macaire and J.-G. Postaire, Comput. Vis. Image Underst. 90(2), 190 (2003), DOI: 10.1016/S1077-3142(03)00025-0. Crossref, ISIGoogle Scholar
    • C. Meurieet al., A supervised segmentation scheme for cancerology color images, ISSPIT (2003) pp. 664–667. Google Scholar
    • C. Meurieet al., IJRA 20(2), (2005). Google Scholar
    • N.   Cristianini and J.   Shawe-Taylor , An Introduction to Support Vector Machines and Other Kernel-Bases Learning Methods ( Cambridge University Press , 2000 ) . CrossrefGoogle Scholar
    • R. Collobert and S. Bengio, Journal of Machine Learning Research 1, 143 (2001), DOI: 10.1162/15324430152733142. ISIGoogle Scholar
    • J. Platt, Advances in Large Margin Classifiers, eds. A. J. Smolaet al. (1999) pp. 61–74. Google Scholar
    • D. Priceet al., Pairwise neural network classifiers with probabilistic outputs, NIPS (1994) pp. 1109–1116. Google Scholar
    • T. Hastie and R. Tibshirani, Classification by pairwise coupling, NIPS (1997) pp. 507–513. Google Scholar
    • T. G. Dietterich and G. Bakiri, JAIR 2, 263 (1995). Crossref, ISIGoogle Scholar
    • M. Moreira and E. Mayoraz, Improved pairwise coupling classification with correcting classifiers, ECML (1998) pp. 160–171. Google Scholar
    • T.-F. Wu, C.-J. Lin and R. C. Weng, JMLR 5, 975 (2004). ISIGoogle Scholar
    • C.   Blake and C.   Merz , Advances in Kernel Methods, Support Vector Learning ( 1998 ) . Google Scholar
    • C. Meurieet al., A comparison of supervised pixels-based color image segmentation methods, application in cancerology, WSEAS Transactions on Computers2 (2003) pp. 739–744. Google Scholar
    • L. I.   Kuncheva , Combining Pattern Classifiers: Methods and Algorithms ( Wiley , 2004 ) . CrossrefGoogle Scholar
    • C.   Bishop , Neural Networks for Pattern Recognition ( Oxford University Press , 1995 ) . Google Scholar
    • S.   Haykin , Neural Networks: A Comprehensive Foundation ( Tom Robbins , 1999 ) . Google Scholar
    • L.   Breiman et al. , Classification and Regression Trees ( Wadsworth and Brooks , 1984 ) . Google Scholar
    • J. R.   Quinlan , C4.5: Programs for Machine Learning ( Morgan Kaufmann , 1993 ) . Google Scholar
    • V.   Kecman , Learning and Soft Computing ( MIT Press , 2001 ) . Google Scholar
    Remember to check out the Most Cited Articles!

    Check out our titles in neural networks today!