NDLI: A New Incremental Learning Support Vector Machine

Content Provider	IEEE Xplore Digital Library
Author	Ying-Chun Zhang Guo-Sheng Hu Feng-Feng Zhu Jin-Lian Yu
Copyright Year	2009
Abstract	A new incremental learning method for support vector machine (SVM) is proposed, which train SVM quickly and incrementally. In this paper, we first choose the violating KKT samples which maybe be new support vector candidates. Then for a given new-added sample, the proposed training method validate whether they are border vectors. If true, we add them to training sample set to retrain support vector machine, otherwise omit it. Hence, the training samples can be reduced and training complexity be lessened. Finally, an incremental algorithm is presented to train SVM by using the selected samples violating KKT conditions. Experiment results show that the test error and support vector number of the proposed algorithm is almost same as those of SMO algorithm, however, the training speed of the new incremental algorithm are more quickly that of SMO method.
Starting Page	7
Ending Page	10
File Size	294395
Page Count	4
File Format	PDF
ISBN	9781424438358
DOI	10.1109/AICI.2009.342
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-11-07
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Machine learning algorithms Candidate support vector Mathematics Support vector machine Support vector machines Learning systems Kernel function Incremental learning Support vector machine classification Training data Machine learning Artificial intelligence Computational intelligence Classification tree analysis
Content Type	Text
Resource Type	Article

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