Learning, Uncertainty, and Information Learning Parameters People学习，不确定，信息的学习参数的人.pptVIP

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Learning, Uncertainty, and Information Learning Parameters People学习，不确定，信息的学习参数的人.ppt

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Learning, Uncertainty, and Information Learning Parameters People学习，不确定，信息的学习参数的人

Learning, Uncertainty, and Information:Learning Parameters Big Ideas November 10, 2004 Roadmap Noisy-channel model: Redux Hidden Markov Models The Model Decoding the best sequence Training the model (EM) N-gram models: Modeling sequences Shannon, Information Theory, and Perplexity Conclusion Bayes and the Noisy Channel Generative and sequence Hidden Markov Models (HMMs) An HMM is: 1) A set of states: 2) A set of transition probabilities: Where aij is the probability of transition qi - qj 3)Observation probabilities: The probability of observing ot in state i 4) An initial probability dist over states: The probability of starting in state i 5) A set of accepting states Three Problems for HMMs Find the probability of an observation sequence given a model Forward algorithm Find the most likely path through a model given an observed sequence Viterbi algorithm (decoding) Find the most likely model (parameters) given an observed sequence Baum-Welch (EM) algorithm Learning HMMs Issue: Where do the probabilities come from? Supervised/manual construction Solution: Learn from data Trains transition (aij), emission (bj), and initial (πi) probabilities Typically assume state structure is given Unsupervised Manual Construction Manually labeled data Observation sequences, aligned to Ground truth state sequences Compute (relative) frequencies of state transitions Compute frequencies of observations/state Compute frequencies of initial states Bootstrapping: iterate tag, correct, reestimate, tag. Problem: Labeled data is expensive, hard/impossible to obtain, may be inadequate to fully estimate Sparseness problems Unsupervised Learning Re-estimation from unlabeled data Baum-Welch aka forward-backward algorithm Assume “representative” collection of data E.g. recorded speech, gene sequences, etc Assign initial probabilities Or estimate from very small labeled sample Compute state sequences given the data I.e. use forward algorithm Update transition, emis