NDLI: Kernel MSE algorithm: a unified framework for KFD, LS-SVM and KRR

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	We generalize the conventional minimum squared error (MSE) method to yield a new nonlinear learning machine by using the kernel idea and adding different regularization terms. We name it kernel minimum squared error (KMSE) algorithm, which can deal with linear and nonlinear classification and regression problems. With proper choices of the output coding schemes and regularization terms, we prove that KMSE is identical to the kernel Fisher discriminant (KFD) except for an unimportant scale factor, and it is directly equivalent to the least square version for support vector machine (LS-SVM). For continuous real output values, we find that KMSE is the kernel ridge regression (KRR) with a bias. Therefore KMSE can act as a general framework that includes KFD, LS-SVM and KRR as its particular cases. In addition, we simplify the formula to estimate the projecting direction of KFD. Experiments on artificial and real world data sets in numerical computation aspects demonstrate that KMSE is a class of powerful kernel learning machines.
Sponsorship	Int. Neural Network Soc.
Starting Page	1486
Ending Page	1491
File Size	496381
Page Count	6
File Format	PDF
ISBN	0780370449
ISSN	10987576
DOI	10.1109/IJCNN.2001.939584
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 Machine learning Least squares methods Support vector machines Automation Intelligent systems Learning systems Support vector machine classification Least squares approximation Bayesian methods
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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