A fast learning method for large scale and multi-class samples of SVM

[摘要] A multi-class classification SVM(Support Vector Machine) fast learning method based on binary tree is presented to solve its low learning efficiency when SVM processing large scale multi-class samples. This paper adopts bottom-up method to set up binary tree hierarchy structure, according to achieved hierarchy structure, sub-classifier learns from corresponding samples of each node. During the learning, several class clusters are generated after the first clustering of the training samples. Firstly, central points are extracted from those class clusters which just have one type of samples. For those which have two types of samples, cluster numbers of their positive and negative samples are set respectively according to their mixture degree, secondary clustering undertaken afterwards, after which, central points are extracted from achieved sub-class clusters. By learning from the reduced samples formed by the integration of extracted central points above, sub-classifiers are obtained. Simulation experiment shows that, this fast learning method, which is based on multi-level clustering, can guarantee higher classification accuracy, greatly reduce sample numbers and effectively improve learning efficiency.

[发布机构] China Changfeng Science Technology Industry Group Corp, Beijing, China^1;Second Institute of China Aerospace Science and Industry Corp, Beijing, China^2;Beijing Aerospace Changfeng Co. Ltd, Beijing, China^3

[效力级别] 机械制造 [学科分类] 航空航天科学

[关键词] Bottom up methods;Classification accuracy;Hierarchy structure;Learning efficiency;Multi-class classification;Negative samples;SVM(support vector machine);Training sample