Research on new reduction strategy of SVM used to large-scale training sample set

• Published in: Proceedings of 2011 International Conference on Electronics and Optoelectronics Vol. 2; pp. V2-5 - V2-8
• Main Authors: Wang Anna, Zhao Fengyun, Li Yunlu, Wang Jinbo
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.07.2011
• Subjects: large-scale training sample; reduction; RNA; Support Vector; Support Vector Machine; Support vector machine classification; Training
• ISBN: 1612842755; 9781612842752
• DOI: 10.1109/ICEOE.2011.6013160

It has become a bottleneck to use Support Vector Machine (SVM) due to such problems as slow learning speed, large buffer memory requirement, low generalization performance and so on, which are caused by large-scale training sample set. Concerning these problems, this paper proposed a new reduction strategy for large-scale training sample set. First authors train an initial classifier with a small training set, which is randomly selected from the original samples, then cut the vector which is not Support Vector with the initial classifier to obtain a small reduction set. Training with this reduction set, final classifier is obtained. Experiments show that the learning strategy not only reduces the cost greatly but also obtains a classifier that has almost the same accuracy as the classifier obtained by training large set directly. In addition, speed of classification is greatly improved.