NDLI: Local Support Vector Machine based on Cooperative Clustering for very large-scale dataset

Content Provider	IEEE Xplore Digital Library
Author	Chuanhuan Yin Yingying Zhu Shaomin Mu Shengfeng Tian
Copyright Year	2012
Description	Author affiliation: School of Computer and Information Technology, Beijing Jiaotong University, 100044, China (Chuanhuan Yin; Yingying Zhu; Shengfeng Tian) \|\| School of Computer and Information Engineering, Shandong Agriculture University, Taian, 271018, China (Shaomin Mu)
Abstract	Local support vector machine (LSVM) has been attracting more and more attention because of its consistency. In LSVM, the training of a standard SVM is transformed to the construction of a set of local model of SVM, each of which is obtained by the training on the neighborhood of a certain sample. This strategy reduces the number of samples in every turn of training for the construction of SVM, but increased the number of local model to be trained. Some methods had been proposed to reduce the number of local model which is needed to be trained. However, theses reduction is not enough for very large-scale dataset. In this paper, we present a new Local Support Vector Machine algorithm based on Cooperative Clustering, namely $C^{2}LSVM$ and do the description of the $C^{2}LSVM$ algorithm and experiment In $C^{2}LSVM,$ the data of training subset will be reduced from thousands down to tens. At the same time, the classification accuracy will be preserved even improved.
Starting Page	88
Ending Page	92
File Size	294838
Page Count	5
File Format	PDF
ISBN	9781457721304
ISSN	21579563
e-ISBN	9781457721335
DOI	10.1109/ICNC.2012.6234598
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-05-29
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Training Local support vector machine support vector machine Clustering algorithms Cooperative Clustering Classification algorithms Complexity theory Kernel Testing
Content Type	Text
Resource Type	Article

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