A two stages sparse SVM training
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.
svm core set fitting-plane sparsity