PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Short Note on Two Output-dependent Hidden Markov Models
Jinghao Xue and Mike Titterington
Pattern Recognition Letters 2007.

Abstract

The purpose of this note is to study the assumption of ``mutual information independence", which is used by~\citet{Zhou:2005} for deriving an output-dependent hidden Markov model, the so-called discriminative HMM (D-HMM), in the context of determining a stochastic optimal sequence of hidden states. The assumption is extended to derive its generative counterpart, the G-HMM. In addition, state-dependent representations for two output-dependent HMMs, namely HMMSDO~\citep{Li:2005} and D-HMM, are presented.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:3381
Deposited By:Mike Titterington
Deposited On:09 February 2008