Unverified50% confidenceFactExact time
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
1
Sources
50%
Confidence
Long-term
Relevance
8/27/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedBecause H(p) is a model-independent constant, minimizing cross-entropy is equivalent to minimizing KL divergence; this equivalence is mathematically exact.79% similarUnverifiedAssuming the data follows a Bernoulli or multinomial distribution, maximizing the likelihood is equivalent to minimizing the cross-entropy loss function.79% similarUnverifiedUnder the uniform transition case, the entropy rate of a Markov chain reaches its maximum value of log N, while under the deterministic transition case the entropy rate is 0.77% similarVerifiedThe cross-entropy loss in classification tasks is essentially the negative of the log-likelihood function, and minimizing cross-entropy is equivalent to maximizing the log-likelihood of the model on the training data.77% similarUnverifiedMinimizing cross-entropy is equivalent to minimizing the KL divergence between the true distribution and the predicted distribution.75% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/809953API
curl https://kongchang.com/api/v1/knowledge/claims/809953MCP
get_claim(id=809953)