NDLI: Tighter Perceptron with improved dual use of cached data for model representation and validation

Content Provider	IEEE Xplore Digital Library
Author	Zhuang Wang Vucetic, S.
Copyright Year	2009
Description	Author affiliation: Center for Information Science and Technology, Department of Computer and Information Sciences, Temple University, Philadelphia, PA 19122, USA (Zhuang Wang; Vucetic, S.)
Abstract	Kernel Perceptrons are represented by a subset of training points, called the support vectors, and their associated weights. To address the issue of unlimited growth in model size during training, budget kernel perceptrons maintain the fixed number of support vectors and thus achieve the constant update time and space complexity. In this paper, a new kernel perceptron algorithm for online learning on a budget is proposed. Following the idea of Tighter Perceptron, upon exceeding the budget, the algorithm removes the support vector with the minimal impact on classification accuracy. To optimize memory use, instead on maintaining a separate validation data set for accuracy estimation, the proposed algorithm only uses the support vectors for both model representation and validation. This is achieved by estimating posterior class probability of each support vector and using this information in validation. The experimental results on 11 benchmark data sets indicate that the proposed algorithm is significantly more accurate than the competing budget kernel perceptrons and that it has comparable accuracy to the resource unbounded perceptrons, including the original kernel perceptron and the Tighter Perceptron that uses whole training data set for validation.
Starting Page	3297
Ending Page	3302
File Size	10043766
Page Count	6
File Format	PDF
ISBN	9781424435487
ISSN	10987576
DOI	10.1109/IJCNN.2009.5178978
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-06-14
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Kernel Management training Training data Neural networks Convergence Fasteners Probability Support vector machines Machine learning
Content Type	Text
Resource Type	Article

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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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