May 2018
Intermediate to advanced
576 pages
14h 42m
English
Content preview from Mastering Machine Learning Algorithms
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Addendum to HMMs
In the previous chapter, we discussed how it's possible to train a HMM using the forward-backward algorithm and we have seen that it is a particular application of the EM algorithm. The reader can now understand the internal dynamic in terms of E and M steps. In fact, the procedure starts with randomly initialized A and B matrices and proceeds in an alternating manner:
- E-Step:
- The estimation of the probability αtij that the HMM is in the state i at time t and in the state j at time t+1 given the observations and the current parameter estimations (A and B)
- The estimation of the probability βti that the HMM is in the state i at time t given the observations and the current parameter estimations (A and B)
- M-Step:
- Computing ...
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