Supervised learning of hidden Markov models for sequence discrimination

Conference · Mon Dec 01 00:00:00 EST 1997

OSTI ID:549015

Mamitsuka, Hiroshi ^[1]

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1. NEC Corp., Kanagawa (Japan)

We present two supervised learning algorithms for hidden Markov models (HMMs) for sequence discrimination. When we model a class of sequences with an HMM, conventional learning algorithms for HMMs have trained the HMM with training examples belonging to the class, i.e. positive examples alone, while both of our methods allow us to use negative examples as well as positive examples. One of our algorithms minimizes a kind of distance between a target likelihood of a given training sequence and an actual likelihood of the sequence, which is obtained by a given HMM, using an additive type of parameter updating based on a gradient-descent learning. The other algorithm maximizes a criterion which represents a kind of ratio of the likelihood of a positive example to the likelihood of the total example, using a multiplicative type of parameter updating which is more efficient in actual computation time than the additive type one. We compare our two methods with two conventional methods on a type of cross-validation of actual motif classification experiments. Experimental results show that in terms of the average number of classification errors, our two methods out-perform the two conventional algorithms. 14 refs., 4 figs., 1 tab.

• Research Organization: Association for Computing Machinery, New York, NY (United States); Sloan (Alfred P.) Foundation, New York, NY (United States)
• OSTI ID: 549015
• Report Number(s): CONF-970137-; TRN: 97:005298-0027
• Resource Relation: Conference: RECOMB `97: 1. annual conference on research in computational molecular biology, Santa Fe, NM (United States), 20-22 Jan 1997; Other Information: PBD: 1997; Related Information: Is Part Of RECOMB 97. Proceedings of the first annual international conference on computational molecular biology; PB: 370 p.
• Country of Publication: United States
• Language: English

Related Subjects

55 BIOLOGY AND MEDICINE
BASIC STUDIES
99 MATHEMATICS
COMPUTERS
INFORMATION SCIENCE
MANAGEMENT
LAW
MISCELLANEOUS
MARKOV PROCESS
VALIDATION
DNA SEQUENCING
ALGORITHMS
MATHEMATICAL MODELS
PARAMETRIC ANALYSIS
STOCHASTIC PROCESSES
COMPUTER CODES
MAXIMUM-LIKELIHOOD FIT
DNA
PROTEINS
AMINO ACID SEQUENCE
EFFICIENCY
ACCURACY
ERRORS
MOLECULAR BIOLOGY
NUCLEOTIDES