Equivalent sample size - PowerPoint PPT Presentation

Machine Learning and Review

Machine Learning and Review Reading: C. 18 Bayesian Approach Each observed training example can incrementally decrease or increase probability of hypothesis instead of eliminate an hypothesis Prior knowledge can be combined with observed data to determine hypothesis

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Bayesian Classifier

Bayesian Classifier. f:X V, finite set of values Instances x  X can be described as a collection of features x = ( x 1 , x 2 , … x n ) x i 2 {0,1} Given an example, assign it the most probable value in V Bayes Rule:

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10/29: Text Classification

10/29: Text Classification. Classification Learning (aka supervised learning). Given labelled examples of a concept (called training examples) Learn to predict the class label of new (unseen) examples

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Basics of Statistical Estimation

Basics of Statistical Estimation. tails. heads. Learning Probabilities: Classical Approach. Simplest case: Flipping a thumbtack. True probability q is unknown. Given iid data, estimate q using an estimator with good properties: low bias, low variance, consistent

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709 views • 23 slides

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