Hybrid pattern recognition using Markov networks
Gregor, J.
Thomason, M.G.
Inst. of Electron. Syst., Aalborg Univ.;
This paper appears in: Pattern Analysis and Machine Intelligence, IEEE Transactions on
Publication Date: Jun 1993
Volume: 15,
Issue: 6
On page(s): 651-656
ISSN: 0162-8828
References Cited: 19
CODEN: ITPIDJ
INSPEC Accession Number: 4465232
Digital Object Identifier: 10.1109/34.216736
Current Version Published: 2002-08-06
Abstract
Markov networks are inferred automatically for different classes
of learning strings. In subsequent string-to-network alignments for test
samples, the networks are used to deduce structural characteristics and
to provide similarity measures. By processing the similarity measures as
numerical-value features, standard nonparametric decision-theoretic
pattern classifiers may be applied to determine class membership. The
nearest-neighbor rule and linear discriminant-function classifiers are
discussed, and their performances are compared with that of a
maximum-likelihood classifier. The hybrid system's ability to determine
string orientation correctly is investigated. Experiments with several
thousand human banded chromosomes are reported
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