TRA:Improved sifting algorithm for incremental SVM learning
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The support vector machine,is reported current research of incremental SVM learning algorithm.The transformation between support vectors and normal vectors during new samples added to support vector set is analyzed.Aimed at the inefficient removing method, an improved sifting algorithm for incremental SVM learning——twice removing algorithm is proposed.In this algorithm,the useless samples are discarded by two useful removing methods,leads to new incremental training choose removing effective dataset instead of using the whole dataset they can not deal easily with very large dadasets,it can reduce subsequence training time.The theoretical analysis and experimental results show that this algorithm can not only improve the training speed,but also guarantee the classification precision.