## 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)

### Keywords:

SVMs; classification; sparse design

SVMlight
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