Active Learning for Sparse Least Squares Support Vector Machines

Abstract

Abstract. For least squares support vector machine (LSSVM) the lack of sparse, while the standard sparse algorithm exist a problem that it need to mark all of training data. We propose an active learning algorithm based on LSSVM to solve sparse problem. This method first construct a minimum classification LSSVM, and then calculate the uncertainty of the sample, select the closest category to mark the sample surface, and finally joined the training set of labeled samples and the establishment of a new classifier, repeat the process until the model accuracy to meet Requirements. 6 provided in the UCI data sets on the experimental results show that the proposed method can effectively improve the sparsity of LSSVM, and can reduce the cost labeled samples.