Combinatorial Semi-supervised Incremental Support Vector Machine Learning Algorithm
GUO Husheng1,2, WANG Wenjian1, 2, PAN Shichao1
1.School of Computer and Information Technology, Shanxi University, Taiyuan 030006 2.Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education,Shanxi University, Taiyuan 030006
Abstract Incremental support vector machine (ISVM) has difficulty in selecting the best incremental sample during each incremental learning step, and therefore the generalization performance of the model is weak. To solve this problem, combinatorial semi-supervised incremental support vector machine learning algorithm (ICS3VM) is proposed. The best incremental sample is selected by combinatorial labeling of the large scale unlabeled samples in batches. The most valuable unlabeled samples in the classification margin are added into the training set each time to correct the model. Meanwhile, the label with the largest margin is regarded as the final label to ensure the accuracy. The experiment on the standard datasets shows the good generalization performance and the high learning efficiency of the proposed ICS3VM.
Corresponding Authors:
WANG Wenjian Corresponding author, born in 1968, Ph.D., professor. Her research interests include computational intelligence and data mining.
About author:: GUO Husheng, born in 1986, Ph.D., lecturer. His research interests include machine learning and data mining.PAN Shichao, born in 1987, master student. Her research interests include machine learning and intelligent computing.
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2016, Vol. 29 
