March 2019
Intermediate to advanced
642 pages
22h 54m
English
Content preview from Python Machine Learning Cookbook - Second Edition
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An HMM is, therefore, a Markov chain in which states are not directly observable. More precisely, it can be understood as follows:
- The chain has a number of states
- The states evolve according to a Markov chain
- Each state generates an event with a certain probability distribution that depends only on the state
- The event is observable, but the state is not
HMMs are particularly known for their applications in the recognition of the temporal pattern of spoken speeches, handwriting, texture recognition, and bioinformatics.
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