Hidden Markov Model Training Algorithm

A Hidden Markov Model Training Algorithm is a generative sequence-model learning algorithm that can by applied by an HMM training system (to solve an HMM training task to produce an HMM model).

• AKA: HMM Modeling Method.
• Context:
  • …
• Counter-Example(s):
  • a Conditional Random Fields Algorithm.
• See: Viterbi Decoding Algorithm.

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References

2013

• http://en.wikipedia.org/wiki/Hidden_Markov_model#Learning
  • The parameter learning task in HMMs is to find, given an output sequence or a set of such sequences, the best set of state transition and output probabilities. The task is usually to derive the maximum likelihood estimate of the parameters of the HMM given the set of output sequences. No tractable algorithm is known for solving this problem exactly, but a local maximum likelihood can be derived efficiently using the Baum–Welch algorithm or the Baldi–Chauvin algorithm. The Baum–Welch algorithm is a special case of the expectation-maximization algorithm.

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