Recap: Conditional Exponential Model

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Presentation transcript:

Recap: Conditional Exponential Model Predication probability Model parameters: For each class y, we have weights wy and threshold cy Maximum likelihood estimation Translation invariance

Modified Conditional Exponential Model Set w1 to be a zero vector and c1 to be zero Predication probability Model parameter estimation

MaxEnt for Classification Problems Favor uniform distributions Maximizing entropy of distribution Consistent with training data Constraints on the mean of input features

Translation Problem Parameters: p(dans), p(en), p(au), p(a), p(pendant) Represent each French word with two features {dans, en} {dans, a} dans 1 en au-cours-de a pendant Empirical Average 0.3 0.5

Constraints

Maximum Entropy Formulation for the Translation Problem Solution: p(dans) = 0.2, p(a) = 0.3, p(en)=0.1, p(au-cours-de) = 0.2, p(pendant) = 0.2