Издательство: | Книга по требованию |
Дата выхода: | июль 2011 |
ISBN: | 978-6-1338-9768-7 |
Объём: | 84 страниц |
Масса: | 147 г |
Размеры(В x Ш x Т), см: | 23 x 16 x 1 |
High Quality Content by WIKIPEDIA articles! In probability theory and information theory, the Kullback–Leibler divergenc (also information divergence, information gain, relative entropy, or KLIC) is a non-symmetric measure of the difference between two probability distributions P and Q. KL measures the expected number of extra bits required to code samples from P when using a code based on Q, rather than using a code based on P. Typically P represents the "true" distribution of data, observations, or a precise calculated theoretical distribution. The measure Q typically represents a theory, model, description, or approximation of P. Although it is often intuited as a distance metric, the KL divergence is not a true metric – for example, the KL from P to Q is not necessarily the same as the KL from Q to P. KL divergence is a special case of a broader class of divergences called f-divergences. Originally introduced by Solomon Kullback and Richard Leibler in 1951 as the directed divergence between two distributions, it is not the same as a divergence in calculus. However, the KL divergence can be derived from the Bregman divergence.
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