NDLI: Large margin kernel pocket algorithm

Content Provider	IEEE Xplore Digital Library
Author	Jianhua Xu Xuegong Zhang Yanda Li
Copyright Year	2001
Description	Author affiliation: Dept. of Autom., Tsinghua Univ., Beijing, China (Jianhua Xu)
Abstract	Two attractive advantages of SVM are the ideas of kernels and of large margin. As a linear learning machine, the original pocket algorithm can handle both linearly and nonlinearly separable problems. In order to improve its classification ability and control its generalization, we generalize the original pocket algorithm by using kernels and adding a margin criterion, and propose its kernel and large margin version, which can be referred to as large margin kernel pocket algorithm (LMKPA). The objective is to maximize both the number of correctly classified samples and the distance between the separating hyperplane and those correctly classified samples closest to the hyperplane, in the feature space realized with the kernels. This new algorithm only utilizes an iterative procedure to implement kernel idea and large margin simultaneously. For the linearly separable problems, LMKPA can find a solution that is not only without error, but also almost equivalent to that of SVM with the large-margin goal. For linearly nonseparable problems, its performance is also very close to that of SVM. Experiments in numeral computation aspects show that the performance of LMKPA is close to that of SVM but the algorithm is much simpler.
Sponsorship	Int. Neural Network Soc.
Starting Page	1480
Ending Page	1485
File Size	507229
Page Count	6
File Format	PDF
ISBN	0780370449
ISSN	10987576
DOI	10.1109/IJCNN.2001.939581
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2001-07-15
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Kernel Support vector machines Support vector machine classification Iterative algorithms Machine learning Machine learning algorithms Intelligent systems Learning systems Robustness Algorithm design and analysis
Content Type	Text
Resource Type	Article

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