Unverified60% confidenceFactExact time
Assuming the data follows a Bernoulli or multinomial distribution, maximizing the likelihood is equivalent to minimizing the cross-entropy loss function.
2
Sources
60%
Confidence
Long-term
Relevance
7/12/2026
First Seen
Sources
A Deep Dive into Core Machine Learning Concepts: Those 'Aha' Moments of Sudden Clarity
redditr/learnmachinelearning7/11/2026
Related Claims
UnverifiedMinimizing cross-entropy is equivalent to minimizing the KL divergence between the true distribution and the predicted distribution.82% similarUnverifiedAssuming the data follows a Gaussian distribution, maximizing the likelihood is equivalent to minimizing the mean squared error.80% 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.80% 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79% 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.78% similar
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