NDLI: Online modeling based on support vector machine

Content Provider	IEEE Xplore Digital Library
Author	Shuzhou Wang
Copyright Year	2009
Description	Author affiliation: School of Computer Technology and Automation, Tianjin Polytechnic University, 300160, China (Shuzhou Wang)
Abstract	Support vector machine (SVM) is a new method based on statistical learning theory. Online algorithms for training SVM are efficient to run, easy to implement comparing with batch algorithms. Presently online algorithms usually do not provide with the ability to explicitly control the number of support vectors. A modified online algorithm for SVM is proposed, witch has a budget parameter to explicitly control the number of support vectors. The proposed algorithm was applied to construct intelligent model of helicopter. It is shown by simulation that the modified online algorithm can reduce the number of support vectors effectively with similar generalization ability.
Starting Page	1188
Ending Page	1191
File Size	110870
Page Count	4
File Format	PDF
ISBN	9781424427222
DOI	10.1109/CCDC.2009.5192324
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-06-17
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Dictionaries Automation Helicopter Online Algorithms Helicopters Statistical learning Support Vector Machine Machine intelligence Support vector machines Support vector machine classification Linear approximation Training data Simulation Model Numerical stability
Content Type	Text
Resource Type	Article

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