Optimized feature extraction and the Bayes decision in feed-forwardclassifier networks
Lowe, D.
Webb, A.R.
R. Signals & Radar Establ., Great Malvern;
This paper appears in: Pattern Analysis and Machine Intelligence, IEEE Transactions on
Publication Date: Apr 1991
Volume: 13,
Issue: 4
On page(s): 355-364
ISSN: 0162-8828
References Cited: 29
CODEN: ITPIDJ
INSPEC Accession Number: 3960794
Digital Object Identifier: 10.1109/34.88570
Current Version Published: 2002-08-06
Abstract
The problem of multiclass pattern classification using adaptive
layered networks is addressed. A special class of networks, i.e.,
feed-forward networks with a linear final layer, that perform
generalized linear discriminant analysis is discussed, This class is
sufficiently generic to encompass the behavior of arbitrary feed-forward
nonlinear networks. Training the network consists of a least-square
approach which combines a generalized inverse computation to solve for
the final layer weights, together with a nonlinear optimization scheme
to solve for parameters of the nonlinearities. A general analytic form
for the feature extraction criterion is derived, and it is interpreted
for specific forms of target coding and error weighting. An important
aspect of the approach is to exhibit how a priori information regarding
nonuniform class membership, uneven distribution between train and test
sets, and misclassification costs may be exploited in a regularized
manner in the training phase of networks
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