Abstract
The small number of support vectors is an important factor for SVM to fast deal with very large scale problems. This paper considers fitting each class of data with a plane by a new model, which captures separability information between classes and can be solved by fast core set methods. Then training on the core sets of the fitting-planes yields a very sparse SVM classifier. The computing complexity of the proposed algorithm is up bounded by \( {\text{\rm O}}(1/\varepsilon ) \). Experimental results show that the new algorithm trains faster than both CVM and SVMperf averagely, and with comparable generalization performance.
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