Statistical pattern recognition: a review
Jain, A.K.
Duin, R.P.W.
Jianchang Mao
Dept. of Comput. Sci. & Eng., Michigan State Univ., East Lansing, MI;
This paper appears in: Pattern Analysis and Machine Intelligence, IEEE Transactions on
Publication Date: Jan 2000
Volume: 22,
Issue: 1
On page(s): 4-37
ISSN: 0162-8828
References Cited: 168
CODEN: ITPIDJ
INSPEC Accession Number: 6525225
Digital Object Identifier: 10.1109/34.824819
Current Version Published: 2002-08-06
Abstract
The primary goal of pattern recognition is supervised or
unsupervised classification. Among the various frameworks in which
pattern recognition has been traditionally formulated, the statistical
approach has been most intensively studied and used in practice. More
recently, neural network techniques and methods imported from
statistical learning theory have been receiving increasing attention.
The design of a recognition system requires careful attention to the
following issues: definition of pattern classes, sensing environment,
pattern representation, feature extraction and selection, cluster
analysis, classifier design and learning, selection of training and test
samples, and performance evaluation. In spite of almost 50 years of
research and development in this field, the general problem of
recognizing complex patterns with arbitrary orientation, location, and
scale remains unsolved. New and emerging applications, such as data
mining, web searching, retrieval of multimedia data, face recognition,
and cursive handwriting recognition, require robust and efficient
pattern recognition techniques. The objective of this review paper is to
summarize and compare some of the well-known methods used in various
stages of a pattern recognition system and identify research topics and
applications which are at the forefront of this exciting and challenging
field
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