Building Support Vector Machines with Reduced Classifier Complexity

Keerthi, S; Chapelle, O; DeCoste, D

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Building Support Vector Machines with Reduced Classifier Complexity

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Chapelle, O
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

Keerthi, S., Chapelle, O., & DeCoste, D. (2006). Building Support Vector Machines with Reduced Classifier Complexity. Journal of Machine Learning Research, 7, 1493-1515. Retrieved from http://jmlr.csail.mit.edu/papers/volume7/keerthi06a/keerthi06a.pdf.

Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-D0CD-B

Abstract

Support vector machines (SVMs), though accurate, are not preferred in applications requiring great classification speed, due to the number of support vectors being large. To overcome this problem we devise a primal method with the following properties: (1) it decouples the idea of basis functions from the concept of support vectors; (2) it greedily finds a set of kernel basis functions of a specified maximum size (dmax) to approximate the SVM primal cost function well; (3) it is efficient and roughly scales as O(ndmax^2) where n is the number of training examples; and, (4) the number of basis functions it requires to achieve an accuracy close to the SVM accuracy is usually far less than the number of SVM support vectors.