NDLI: A new incremental learning algorithm based on hyper-sphere SVM

Content Provider	IEEE Xplore Digital Library
Author	Yuping Qin Qiangkui Leng Xiangna Meng Qian Luo
Copyright Year	2010
Description	Author affiliation: College of Information Science and Technology, Bohai University, Jinzhou, China (Yuping Qin; Qiangkui Leng; Xiangna Meng; Qian Luo)
Abstract	A sample and class incremental learning algorithm based on hyper-sphere support vector machine is proposed. For every class, hyper-sphere support vector machine is used to get the smallest hyper-sphere that contains most samples of the class, which can divide the class samples from others. In the process of incremental learning, the hyper-sphere of every new class are trained, and the history hyper-spherees that have something to do with the new incremental samples are retrained. For the sample to be classified, the distances from it to the centre of every hyper-spheres are used to confirm the class that the sample belongs to. The experimental results show that the algorithm has a higher performance on training speed, classification speed, and classification precision.
Starting Page	2340
Ending Page	2343
File Size	236456
Page Count	4
File Format	PDF
ISBN	9781424459315
e-ISBN	9781424459346
DOI	10.1109/FSKD.2010.5569831
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-08-10
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training support vector machine hyper-sphere incremental learning Support vector machine classification Machine learning Classification algorithms Kernel Testing
Content Type	Text
Resource Type	Article

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