Hidden Markov Model
A statistical model in which the system's internal states are not directly observable and must be inferred from noisy observation sequences. The model assumes states evolve according to a Markov process while observations depend probabilistically on the current hidden state. Hidden Markov Models are widely applied to sequential data modeling and analysis tasks such as speech recognition and biological sequence analysis.
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In animal communication research, commonly used machine learning methods include unsupervised clustering, supervised classification and sequence modeling (such as hidden Markov models or recurrent neural networks).
On a four-model hidden Markov family, learning an unknown Bayesian decision requires Θ(l⁻²) records.
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In animal communication research, commonly used machine learning methods include unsupervised clustering, supervised classification and sequence modeling (such as hidden Markov models or recurrent neural networks).
50%UnverifiedOn a four-model hidden Markov family, learning an unknown Bayesian decision requires Θ(l⁻²) records.
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