Incremental HMM training applied to ECG signal analysis
Computers in Biology and Medicine
Incremental estimation of discrete hidden Markov models based on a new backward procedure
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
Incremental behavior modeling and suspicious activity detection
Pattern Recognition
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Conventional training of a hidden Markov model (HMM) is performed by an expectation-maximization algorithm using a maximum likelihood (ML) criterion. It was reported that, using an incremental variant of maximum a posteriori estimation, substantial speed improvements could be obtained. The approach requires a prior distribution when the training starts, although it is difficult to find an appropriate prior for some cases. This paper presents a new approach for achieving an efficient training of HMM parameters using the standard ML criterion. A prior distribution is not required. The algorithm sequentially selects a subset of data from the training set, updates the parameters from the subset, then iterates until convergence. There is a solid theoretical foundation that ensures a monotone likelihood improvement; thus stable convergence is guaranteed. Experimental results indicate substantially faster convergence than the standard batch training algorithm while holding the same level of recognition performance.