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Sparseness of Least Squares Support Vector Machines Based on Active Learning


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Sparseness of Least Squares Support Vector Machines Based on Active Learning
YU Zheng-tao12ZOU Jun-jie12ZHAO Xing12SU Lei12MAO Cun-li12
1.School of Information Engineering and Automation;2.Key Laboratory of Intelligent Information Processing, Kunming University of Science and Technology,Kunming 650051,China
least squares support vector machines sparseness active learning classification
To solve the sparseness problem of least squares support vector machine(LSSVM)learning process,this paper proposes a learning algorithm of LSSVM data sparseness based on active learning.This algorithm first selects initial samples based on a kernel clustering method and constructs a minimum classification using LSSVM,calculates the sample distribution under the action of the classifier,and labels the samples closest to hyper planes.These labeled samples are finally added into the training sets to train a new classifier,and the processes are repeated until the model accuracy meets requirements.The LSSVM sparse model of some samples are established.Experiments on the University of California Irvine(UCI)data sets show that the proposed algorithm can increase the sparseness of LSSVM by more 46 percent and reduce the cost of labeling samples


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Last Update: 2012-10-12