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Hybrid pattern recognition using Markov networks

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2 Author(s)
J. Gregor ; Inst. of Electron. Syst., Aalborg Univ., Denmark ; M. G. Thomason

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

Published in:

IEEE Transactions on Pattern Analysis and Machine Intelligence  (Volume:15 ,  Issue: 6 )