Hybrid pattern recognition using Markov networks
Jens Gregor, Michael G. Thomason · IEEE Transactions on Pattern Analysis and Machine Intelligence · 1993
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.>