Two New Decomposition Algorithms for Training Bound-Constrained Support Vector Machines*

[1] Asuncion A., Newman D., UCI Machine Learning Repository, University of California, Irvine, School of Information and Computer Sciences (2007) http://www. ics.uci.edu/~mlearn/MLRepository.html.Search in Google Scholar

[2] Boser B., Guyon I., Vapnik V.A., A training algorithm for optimal margin classifiers, Proceedings of the 5th Annual ACM Workshop on Computational Learning Theory, ACM Press, 1992, 144-152.10.1145/130385.130401Search in Google Scholar

[3] Bottou L., Stochastic gradient descent examples, 2007, http://leon.bottou. org/projects/sgd.Search in Google Scholar

[4] Cortes C., Vapnik V., Support-vector networks, Machine Learning, 20, 3, 1995, 273-297.10.1007/BF00994018Search in Google Scholar

[5] Franc V., Sonnenburg S., Optimized cutting plane algorithm for support vector machines, ICML 08: Proceedings of the 25th international conference on Machine learning, ACM Press 2008, 320-327.10.1145/1390156.1390197Search in Google Scholar

[6] Gertz E., Wright S., Object-oriented software for quadratic programming, ACM Transactions on Mathematical Software, 29, 2001, 58-81.10.1145/641876.641880Search in Google Scholar

[7] Hsieh C., Chang K., Li C.J., A comparison of methods for multi-class support vector machines, IEEE Transactions on Neural Networks, 13, 2002, 415-425.10.1109/72.99142718244442Search in Google Scholar

[8] Hsieh C., Chang K., Lin C.J., Keerthi S., Sundararajan S., A dual coordinate descent method for large-scale linear SVM, Proceedings of the 25th international conference on Machine learning, ACM, 2008, 408-415.10.1145/1390156.1390208Search in Google Scholar

[9] Hsu C.W., Lin C.J., A simple decomposition method for support vector machines, Machine Learning, 46, 1-3, 2002, 291{314.10.1023/A:1012427100071Search in Google Scholar

[10] Joachims T., SVMlight, http://svmlight.joachims.org/.Search in Google Scholar

[11] Joachims T., Training linear SVMs in linear time, ACM SIGKDD International Conference On Knowledge Discovery and Data Mining, 2006, 217-226.10.1145/1150402.1150429Search in Google Scholar

[12] Joachims T., Finley T., Yu C.N., Cutting-plane training of structural svms, Machine Learning, 77, 1, 2009, 27-59.10.1007/s10994-009-5108-8Search in Google Scholar

[13] Joachims T., Yu C.N., Sparse kernel svms via cutting-plane training, Machine Learning, Special Issue for European Conference on Machine Learning, 76, 2-3, 2009, 179-193.10.1007/s10994-009-5126-6Search in Google Scholar

[14] Mangasarian O., Musicant D., Successive overrelaxation for support vector machines, IEEE Transactions on Neural Networks, 10, 5, 1999, 1032-1037.10.1109/72.78864318252605Search in Google Scholar

[15] Mangasarian O., Musicant D., Lagrangian support vector machines, Journal of Machine Learning Research, 1, 2001, 161-177.Search in Google Scholar

[16] Mercer J., Functions of positive and negative type and their connection with the theory of integral equations, Philosophical Transactions of the Royal Society of London, 1909.Search in Google Scholar

[17] Shalev-Shwartz S., Singer Y., Srebro N., Cotter A., Pegasos: Primal Estimated sub-Gradient Solber for SVM, Mathematical Programming, 127, 1, 2011, 3-30.10.1007/s10107-010-0420-4Search in Google Scholar

[18] Sun W.Y., Yuan Y.X., Optimization Theory and Methods: Nonlinear Program- ming, Springer, New York, USA, 2006.Search in Google Scholar

[19] Frieb T., Cristianini N., Campbell C., The kernel-adatron algorithm: a fast and simple learning procedure for support vector machines, Proceedings of the Fifteenth International Conference on Machine Learning, Morgan Kaufmann Publishers, 1998.Search in Google Scholar

[20] Vapnik V., The Nature of Statistical Learning Theory, Springer-Verlag New York, Inc., New York, NY, USA 1995.10.1007/978-1-4757-2440-0Search in Google Scholar

[21] Vapnik V., Statistical Learning TheoryStatistical Learning Theory, Wiley- Interscience, September, 1998.Search in Google Scholar

[22] Yuan G.X., Ho C.H., Lin C.J., Recent Advances of Large-scale Linear Classifi- cation, Proceedings of the IEEE, 100, 2012, 2584-2603.10.1109/JPROC.2012.2188013Search in Google Scholar

[23] Zanni L., Serafini T., Zanghirati G., Parallel software for training large scale support vector machines on multiprocessor systems, Journal of Maching Learning Research, 7, 2006, 1467-1492.Search in Google Scholar

[24] Osuna E., Freund R., Girosi F., Training support vector machines: An application to face detection, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1997, 276-285.Search in Google Scholar

[25] Arnosti N.A., Kalita J.K., Cutting Plane Training for Linear Support Vector Machines, IEEE Transactions on Knowledge and Data Engineering, 25, 2013, 1186-1190.10.1109/TKDE.2011.247Search in Google Scholar

[26] Platt J.-C., Fast training of support vector machines using sequential minimal optimization, in: B. Scholkopf, C.J.C. Burges, A.J. Smola(Eds.), Advances in kernel methods-support vector learning, MIT press, 1999, 185-208.10.7551/mitpress/1130.003.0016Search in Google Scholar

[27] Joachims T., Making large-scale SVM learning practical, in: B. Scholkopf, C.J.C. Burges, A.J. Smola(Eds.), Advances in kernel methods-support vector learning, Cambridge, MA, MIT press, 1998.Search in Google Scholar

[28] Saunders C., Stitson M.O., Weston J., Bottou L., Scholkopf B., Smola A., Support vector machine reference manual, Egham, UK, Royal Holloway, University of London, Technical Report, NO.CSD-TR-98-03, 1998.Search in Google Scholar

[29] Tian Y.J., Qi Z.Q., Ju X.C., Shi Y., Liu X.H., Nonparallel support vector machines for pattern classification, IEEE Trans. Cybernetics, 44, 7, 2013, 1067-1079.10.1109/TCYB.2013.227916724013833Search in Google Scholar

[30] Qi Z., Tian Y., Shi Y., Successive Overrelaxation for Lapiacian Support Vector Machine, IEEE Transaction on Neural Networks and Learning System, DOI: 10.1109/TNNLS.2014.2320738, 2014.10.1109/TNNLS.2014.232073825961091Search in Google Scholar

[31] Qi Z., Tian Y., Shi Y., Structural Twin Support Vector Machine for Classification, Knowledge-Based Systems, 43, 2013, 74-81.10.1016/j.knosys.2013.01.008Search in Google Scholar