NDLI: A SVC Iterative Learning Algorithm Based on Sample Selection for Large Samples

Content Provider	IEEE Xplore Digital Library
Author	Zi-Jie Chen Bo Liu Xu-Peng He
Copyright Year	2007
Description	Author affiliation: Guangdong Pharm. Univ., Guangzhou (Zi-Jie Chen)
Abstract	This paper focuses on an effective and efficient support vector machine classification training algorithm for large samples. This method is called 'SVC iterative learning algorithm based on sample selection (short for SVCI)'. Initially, a sample selection strategy based on fuzzy c-means clustering is performed to select partial samples as the first training set, so that common decomposition algorithms are competent and efficient in the small-scale sub-learnings. Furthermore, iterative training is applied to improve the rough learning machine to guarantee performance. Before a new training, another sample selection strategy is carried out to define the new training set. The final optimal classifier is approximate to the one of the original problem. Experiments on several large-scale UCI data sets show that, this iterative algorithm can converge quickly, double training speed and cut down the number of support vectors by a half with losing quite little accuracy.
Starting Page	3308
Ending Page	3313
File Size	633141
Page Count	6
File Format	PDF
ISBN	9781424409723
DOI	10.1109/ICMLC.2007.4370719
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-08-19
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Static VAr compensators Iterative algorithms Support vector machines Support vector machine classification Clustering algorithms Machine learning Large-scale systems Cybernetics Pharmaceutical technology Iterative methods Iterative algorithm Support vector machine Large samples Sample selection Fuzzy c-means
Content Type	Text
Resource Type	Article

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