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dc.contributor.authorMutter, Stefan
dc.contributor.authorPfahringer, Bernhard
dc.contributor.authorHolmes, Geoffrey
dc.coverage.spatialConference held at Auckland, New Zealanden_NZ
dc.identifier.citationMutter, S., Pfahringer, B.& Holmes, G. (2008). Propositionalisation of Profile Hidden Markov Models for Biological Sequence Analysis. In W. Wobcke & M. Zhang(Eds), Proceedings of 21st Australasian Joint Conference on Artificial Intelligence Auckland, New Zealand, December 1-5, 2008(pp. 278-288 ). Berlin, Germany: Springer.en
dc.description.abstractHidden Markov Models are a widely used generative model for analysing sequence data. A variant, Profile Hidden Markov Models are a special case used in Bioinformatics to represent, for example, protein families. In this paper we introduce a simple propositionalisation method for Profile Hidden Markov Models. The method allows the use of PHMMs discriminatively in a classification task. Previously, kernel approaches have been proposed to generate a discriminative description for an HMM, but require the explicit definition of a similarity measure for HMMs. Propositionalisation does not need such a measure and allows the use of any propositional learner including kernel-based approaches. We show empirically that using propositionalisation leads to higher accuracies in comparison with PHMMs on benchmark datasets.en
dc.sourceAI 2008en_NZ
dc.subjectcomputer scienceen
dc.subjectHidden Markov Modelen
dc.subjectMachine learning
dc.titlePropositionalisation of Profile Hidden Markov Models for Biological Sequence Analysisen
dc.typeConference Contributionen
dc.relation.isPartOfProc Twenty-first Australian Joint Conference on Artificial Intelligenceen_NZ
pubs.volumeLecture Notes in Artificial Intelligence 5360en_NZ

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