Support vector mixture for classification and regression problems
Tin-Yau Kwok, J.
Dept. of Comput. Studies, Hong Kong Baptist Univ., Kowloon Tong;
This paper appears in: Pattern Recognition, 1998. Proceedings. Fourteenth International Conference on
Publication Date: 16-20 Aug 1998
Volume: 1,
On page(s): 255-258 vol.1
Meeting Date: 08/16/1998 - 08/20/1998
Location: Brisbane, Qld., Australia
ISBN: 0-8186-8512-3
References Cited: 8
INSPEC Accession Number: 6091154
Digital Object Identifier: 10.1109/ICPR.1998.711129
Posted online: 2002-08-06 21:56:42.0
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
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