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JMLR
2006

Building Support Vector Machines with Reduced Classifier Complexity

8 years 9 months ago
Building Support Vector Machines with Reduced Classifier Complexity
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(nd2 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.
S. Sathiya Keerthi, Olivier Chapelle, Dennis DeCos
Added 13 Dec 2010
Updated 13 Dec 2010
Type Journal
Year 2006
Where JMLR
Authors S. Sathiya Keerthi, Olivier Chapelle, Dennis DeCoste
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