NDLI: Solving Nonstationary Classification Problems With Coupled Support Vector Machines

Content Provider	IEEE Xplore Digital Library
Author	Grinblat, G.L. Uzal, L.C. Ceccatto, H.A. Granitto, P.M.
Copyright Year	1990
Abstract	Many learning problems may vary slowly over time: in particular, some critical real-world applications. When facing this problem, it is desirable that the learning method could find the correct input-output function and also detect the change in the concept and adapt to it. We introduce the time-adaptive support vector machine (TA-SVM), which is a new method for generating adaptive classifiers, capable of learning concepts that change with time. The basic idea of TA-SVM is to use a sequence of classifiers, each one appropriate for a small time window but, in contrast to other proposals, learning all the hyperplanes in a global way. We show that the addition of a new term in the cost function of the set of SVMs (that penalizes the diversity between consecutive classifiers) produces a coupling of the sequence that allows TA-SVM to learn as a single adaptive classifier. We evaluate different aspects of the method using appropriate drifting problems. In particular, we analyze the regularizing effect of changing the number of classifiers in the sequence or adapting the strength of the coupling. A comparison with other methods in several problems, including the well-known STAGGER dataset and the real-world electricity pricing domain, shows the good performance of TA-SVM in all tested situations.
Sponsorship	IEEE Computational Intelligence Society
Page Count	15
File Size	771164
Starting Page	37
Ending Page	51
File Format	PDF
ISSN	10459227
Volume Number	22
Issue Number	1
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-01-01
Publisher Place	U.S.A.
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Estimation Kernel Extrapolation Training Couplings Accuracy support vector machine Adaptive methods drifting concepts
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Computer Networks and Communications Computer Science Applications Software

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