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Reduced-rank hidden Markov models
Byron Boots
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Oznake: computer science;machine learning;markov processes
Hsu et al.(2009) recently proposed an efficient, accurate spectral learning algorithm for Hidden Markov Models (HMMs). In this paper we relax their assumptions and prove a tighter finite-sample error bound for the case of Reduced-Rank HMMs, i.e., HMMs with low-rank transition matrices. Since rank-k ...
Leto: 2010 Vir: videolectures.net
Št. zadetkov: 1
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