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• W1581013037 abstract "Viterbi Training (VT) provides a fast but inconsistent estimator of Hidden Markov Models (HMM). The inconsistency is alleviated with little extra computation when we enable VT to asymptotically fix the true values of the parameters. This relies on infinite Viterbi alignments and associated with them limiting probability distributions. First in a sequel, this paper is a proof of concept; it focuses on mixture models, an important but special case of HMM where the limiting distributions can be calculated exactly. A simulated Gaussian mixture shows that our central algorithm (VA1) can significantly improve the accuracy of VT with little extra cost. Next in the sequel, we present elsewhere a theory of the adjusted VT for the general HMMs, where the limiting distributions are more challenging to find. Here, we also present another, more advanced correction to VT, verify its fast convergence and high accuracy; its computational feasibility requires additional investigation." @default.
• W1581013037 created "2016-06-24" @default.
• W1581013037 creator A5060111294 @default.
• W1581013037 creator A5079016144 @default.
• W1581013037 date "2005-01-01" @default.
• W1581013037 modified "2023-10-03" @default.
• W1581013037 title "Adjusted Viterbi Training : a proof of concept" @default.
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• W1581013037 hasPublicationYear "2005" @default.
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