A NOVEL LEARNINGALGORITHM FOR DATA CLASSIFICATION WITH RADIAL BASIS FUNCTION NETWORKS
Date Issued
2004
Date
2004
Author(s)
DOI
20060927122841304963
Abstract
This paper proposes a novel learning algorithm for
constructing data classifiers with radial basis function
(RBF) networks. The RBF networks constructed with
the proposed learning algorithm generally are able to
deliver the same level of classification accuracy as the
support vector machines (SVM). One important
advantage of the proposed learning algorithm, in
comparison with the support vector machines, is that the
proposed learning algorithm normally takes far less time
to figure out optimal parameter values with cross
validation. A comparison with the SVM is of interest,
because it has been shown in a number of recent studies
that the SVM generally is able to deliver higher level of
accuracy than the other existing data classification
algorithms. The proposed learning algorithm works by
constructing one RBF network to approximate the
probability density function of each class of objects in
the training data set. The main distinction of the
proposed learning algorithm is how it exploits local
distributions of the training samples in determining the
optimal parameter values of the basis functions. As the
proposed learning algorithm is instance-based, the data
reduction issue is also addressed in this paper. One
interesting observation is that, for all three data sets used
in data reduction experiments, the number of training
samples remaining after a naïve data reduction
mechanism is applied is quite close to the number of
support vectors identified by the SVM software.
Subjects
Radial basis function network
Data
classification
classification
Machine learning
Publisher
臺北市:國立臺灣大學資訊工程學系
Type
other
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