Research in SVM Sample Optimizes of ISODATA Algorithm

Abstract

Support Vector Machine is widely used in data classification, but in the case of more training samples, the training time is longer. To solve this problem, use the ISODATA clustering algorithm to cluster samples to obtain the new cluster center, together with high similarity to the error for the sample to form a new cluster of training samples, training support vector machines. So that a solution of high similarity to repeat the training samples of similar problems, while focusing on the easily lead to wrong classification of the training samples. The support vector machine classification accuracy can be improved, and also reduces the training time, to make it more convenient for engineering application.