TANG Ting-long,GUAN Qiu,WU Yi-rong.Support vector machine incremental learning triggered by wrongly predicted samples[J].Optoelectronics Letters,2018,14(3):232-235 Support vector machine incremental learning triggered by wrongly predicted samples Author Name Affiliation TANG Ting-long  Collaborative Innovation Center of Yangtze River Delta Region Green Pharmaceuticals, Zhejiang University of Technology, Hangzhou 310023, China College of Computer and Information Technology, China Three Gorges University, Yichang 443002, China  GUAN Qiu  College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310032, China  WU Yi-rong  College of Computer and Information Technology, China Three Gorges University, Yichang 443002, China Department of Radiology, University of Wisconsin-Madison, Madison 53792, USA  Abstract:       According to the classic Karush-Kuhn-Tucker (KKT) theorem, at every step of incremental support vector machine (SVM) learning, the newly adding sample which violates the KKT conditions will be a new support vector (SV) and migrate the old samples between SV set and non-support vector (NSV) set, and at the same time the learning model should be updated based on the SVs. However, it is not exactly clear at this moment that which of the old samples would change between SVs and NSVs. Additionally, the learning model will be unnecessarily updated, which will not greatly increase its accuracy but decrease the training speed. Therefore, how to choose the new SVs from old sets during the incremental stages and when to process incremental steps will greatly influence the accuracy and efficiency of incremental SVM learning. In this work, a new algorithm is proposed to select candidate SVs and use the wrongly predicted sample to trigger the incremental processing simultaneously. Experimental results show that the proposed algorithm can achieve good performance with high efficiency, high speed and good accuracy. Hits: 3618 Download times: 0 View Full Text    Download reader