Moderating the outputs of support vector machine classifiers
Kwok, J.T.-Y.
Dept. of Comput. Sci., Hong Kong Baptist Univ., Kowloon Tong;
This paper appears in: Neural Networks, IEEE Transactions on
Publication Date: Sep 1999
Volume: 10,
Issue: 5
On page(s): 1018-1031
ISSN: 1045-9227
References Cited: 45
CODEN: ITNNEP
INSPEC Accession Number: 6362643
Digital Object Identifier: 10.1109/72.788642
Posted online: 2002-08-06 22:37:20.0
Abstract
In this paper, we extend the use of moderated outputs to the
support vector machine (SVM) by making use of a relationship between SVM
and the evidence framework. The moderated output is more in line with
the Bayesian idea that the posterior weight distribution should be taken
into account upon prediction, and it also alleviates the usual tendency
of assigning overly high confidence to the estimated class memberships
of the test patterns. Moreover, the moderated output derived here can be
taken as an approximation to the posterior class probability. Hence,
meaningful rejection thresholds can be assigned and outputs from several
networks can be directly compared. Experimental results on both
artificial and real-world data are also discussed
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