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What is HMM Lecture2 Nov17 about?

The document discusses three classic Hidden Markov Model (HMM) problems: decoding the most likely state sequence from an output sequence, learning model parameters from observed sequences, and the Viterbi algorithm for finding the most probable path. It also covers the E-M (Estimate-Maximize) algorithm for learning HMM parameters, detailing the Forward and Backward algorithms used to compute probabilities. The document includes examples of HMM training data and outlines the steps to re-estimate transition a

Author

tuli0001

Language

EN