The optimization of large-scale SVM using nest template ant clustering in kernel space

If the training data sets are large-scale, the SVM models turn into large-scale quadratic programming problems and are hard to be effectively solved. That is the main reason why the learning efficiency of SVM is so low on large-scale data sets. According to the principle of SVM, the border support vectors play a decisive role on the SVM decision hyper-plane. The nest template ant clustering algorithm in kernel space is proposed. The algorithm is used to extract border support vectors from large-scale training datasets. When using the less border support vectors to train SVM, the scale of SVM is reduced and the training performance is improved. The algorithm has better adaptability than the kernel K-means algorithm. Experimental results on UCI datasets show that the algorithm is effective, and the classification accuracy of SVM is still maintained.