Keywords

#cross-entropy

Cross-entropy

Information-theoretic measure

In information theory, the cross-entropy between two probability distributions and , over the same underlying set of events, measures the average number of bits needed to identify an event drawn from the set when the coding scheme used for the set is optimized for an estimated probability distribution , rather than the true distribution .

Mon 22nd

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Cross-entropy Entropy (information theory) Cross-entropy method Kullback–Leibler divergence Ensemble learning Principle of maximum entropy Perplexity Tsallis entropy Maximum likelihood estimation Neural machine translation
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