Partially Verified65% confidenceFactExact time
The more certain a distribution is, the lower its entropy and the shorter the required encoding; the more random a distribution is (such as a uniform distribution), the higher its entropy.
3
Sources
65%
Confidence
Long-term
Relevance
8/2/2026
First Seen
Sources
From Entropy to Cross-Entropy: An Information-Theoretic Derivation of the Logistic Regression Loss Function
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UnverifiedMinimizing cross-entropy is equivalent to minimizing the KL divergence between the true distribution and the predicted distribution.81% similarUnverifiedAssuming the data follows a Bernoulli or multinomial distribution, maximizing the likelihood is equivalent to minimizing the cross-entropy loss function.80% similarUnverifiedFinite-sample estimation of entropy rate faces a systematic negative bias problem, where plug-in estimators tend to underestimate the true entropy; this can be compensated using the Miller-Madow correction by adding a (k-1)/(2N) term.76% similarUnverifiedWhen calculated per byte, Shannon Entropy ranges from 0 to 8, with a value close to 8 indicating that the data is nearly completely random.75% similarUnverified微分熵可以取负值、不具有坐标不变性，在非线性变量变换下会发生改变70% similar
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