DocumentCode :
2704705
Title :
Support vector mixture for classification and regression problems
Author :
Kwok, James Tin-Yau
Author_Institution :
Dept. of Comput. Studies, Hong Kong Baptist Univ., Kowloon Tong, Hong Kong
Volume :
1
fYear :
1998
fDate :
16-20 Aug 1998
Firstpage :
255
Abstract :
We study the incorporation of the support vector machine (SVM) into the (hierarchical) mixture of experts model to form a support vector mixture. We show that, in both classification and regression problems, the use of a support vector mixture leads to quadratic programming (QP) problems that are very similar to those for a SVM, with no increase in the dimensionality of the QP problems. Moreover, a support vector mixture, besides allowing for the use of different experts in different regions of the input space, also supports easy combination of different architectures such as polynomial networks and radial basis function networks
Keywords :
feedforward neural nets; learning systems; pattern classification; quadratic programming; statistical analysis; dimensionality; mixture of experts model; pattern classification; polynomial networks; quadratic programming; radial basis function networks; regression analysis; support vector machine; Databases; Iron; Jacobian matrices; Learning systems; Piecewise linear techniques; Postal services; Quadratic programming; Risk management; Support vector machine classification; Support vector machines;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Pattern Recognition, 1998. Proceedings. Fourteenth International Conference on
Conference_Location :
Brisbane, Qld.
ISSN :
1051-4651
Print_ISBN :
0-8186-8512-3
Type :
conf
DOI :
10.1109/ICPR.1998.711129
Filename :
711129
Link To Document :
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