Unverified60% confidenceFactExact time
Minimizing cross-entropy is equivalent to minimizing the KL divergence between the true distribution and the predicted distribution.
2
Sources
60%
Confidence
Long-term
Relevance
7/19/2026
First Seen
Sources
Related Claims
UnverifiedAssuming the data follows a Bernoulli or multinomial distribution, maximizing the likelihood is equivalent to minimizing the cross-entropy loss function.82% similarPartially VerifiedThe 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.81% similarUnverifiedBecause H(p) is a model-independent constant, minimizing cross-entropy is equivalent to minimizing KL divergence; this equivalence is mathematically exact.77% similarUnverified交叉熵损失函数 H(p,q) = -Σp(x)log q(x) 的最小值在q=p时取到，等于真实分布的熵75% 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.75% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/563758API
curl https://kongchang.com/api/v1/knowledge/claims/563758MCP
get_claim(id=563758)