BCP and ZQP Strategies to Reduce the SVM Training Time

The Support Vector Machine (SVM) is awell known method used for classification, regression and density estimation. Training a SVM consists in solving a Quadratic Programming (QP) problem. The QP problemis very resource consuming (both computational time and computational memory), because the quadratic form is dense and the memory requirements grow square the number ofdata points.In order to increase the training speed of SVM's, this paperproposes a combination of two methods, the BCP algorithm(Barycentric Correction Procedure), [15], to find, heuristically,training points with a high probability to be Support Vectors,and the ZQP algorithm, [10], to solve the reduced problem.

[1]  Zhi-Jie He,et al.  A new fast training algorithm for SVM , 2008, 2008 International Conference on Machine Learning and Cybernetics.

[2]  デバラコタ、パンデゥ,et al.  Pattern classification method , 2007 .

[3]  Yuval Rabani,et al.  Linear Programming , 2007, Handbook of Approximation Algorithms and Metaheuristics.

[4]  Rodolfo Ibarra-Orozco,et al.  A Comparison of Different Initialization Strategies to Reduce the Training Time of Support Vector Machines , 2005, ICANN.

[5]  Juan Frausto Solís,et al.  Increasing the Training Speed of SVM, the Zoutendijk Algorithm Case , 2005, ISSADS.

[6]  Liu Fang,et al.  A pattern classification method based on GA and SVM , 2002, 6th International Conference on Signal Processing, 2002..

[7]  Narendra Ahuja,et al.  A geometric approach to train support vector machines , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[8]  Vladimir Vapnik,et al.  An overview of statistical learning theory , 1999, IEEE Trans. Neural Networks.

[9]  John C. Platt,et al.  Fast training of support vector machines using sequential minimal optimization, advances in kernel methods , 1999 .

[10]  Catherine Blake,et al.  UCI Repository of machine learning databases , 1998 .

[11]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[12]  Federico Girosi,et al.  Support Vector Machines: Training and Applications , 1997 .

[13]  Mokhtar S. Bazaraa,et al.  Nonlinear Programming: Theory and Algorithms , 1993 .