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  Building support vector machines with reduced classifier complexity [12 citations — 1 self]

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by S. Sathiya Keerthi, Olivier Chapelle, Dennis Decoste, P. Bennett, Emilio Parrado-hernández
Journal of Machine Learning Research
http://www.jmlr.org/papers/volume7/keerthi06a/keerthi06a.pdf
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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(nd 2 max) 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.

Citations

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