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1999
Journal Article
Titel
Online Estimation of Hidden Markov Models
Abstract
We present a novel and simple online estimation algorithm for hidden Markov models, with memory requirements independent of the data length. The transition matrices and the state distribution are obtained at any instant as contractions fof tensorial quantities, which are iteratively reestimated.