Unverified50% confidenceFactExact time
Because H(p) is a model-independent constant, minimizing cross-entropy is equivalent to minimizing KL divergence; this equivalence is mathematically exact.
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8/4/2026
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Related Claims
UnverifiedMinimizing cross-entropy is equivalent to minimizing the KL divergence between the true distribution and the predicted distribution.77% similarUnverifiedAssuming the data follows a Bernoulli or multinomial distribution, maximizing the likelihood is equivalent to minimizing the cross-entropy loss function.69% 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 0.68% 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.65% similarUnverified线性回归的最小二乘损失与逻辑回归的交叉熵损失分别对应高斯噪声假设与伯努利分布假设下的最大似然估计64% similar
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