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Building support vector machines with reduced classifier complexity. (English) Zbl 1222.68230

Summary: 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 \((d_{\text{max}})\) to approximate the SVM primal cost function well; (3) it is efficient and roughly scales as \(O(nd_{\text{max}}^{2})\) 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.

MSC:

68T05 Learning and adaptive systems in artificial intelligence
62H30 Classification and discrimination; cluster analysis (statistical aspects)

Software:

SVMlight
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