NDLI: A Max Modular Support Vector Machine and Its Variations for Pattern Classification

Content Provider	IEEE Xplore Digital Library
Author	Hui-Cheng Lian
Copyright Year	2009
Abstract	In this paper, we propose a max modular support vector machine (M2-SVM) and its two variations for pattern classification. The basic idea behind these methods is to decompose training samples of one class into several parts and learn each part by one modular classifier independently. To implement these methods, a ‘part-against-others’ training strategy and a max modular combination principle are proposed. Also, a down-sampling technique is employed to improve the training without losing any information of positive sample. Finally, a variation called feature-constructed M2-SVM (FM2-SVM) is proposed,which selects features of each subset for classification in one modular. Experimental results show that FM2-SVM can not only improve the precision of classifying but also reduce the dimension of input features. Performances of the proposed methods are shown to be superior to traditional SVMs as well as M3-SVM and KNN on artificial data, UCI Forest Cover type data, CASPEAL face database and AR face database.
Starting Page	559
Ending Page	564
File Size	1345145
Page Count	6
File Format	PDF
ISBN	9780769536415
DOI	10.1109/ICIS.2009.54
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-06-01
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Support vector machine classification Pattern classification Spatial databases Face detection Information science Optimization methods Parallel machines Hardware Large-scale systems
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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