In information theory, the cross-entropy between two probability distributions p {\displaystyle p} and q {\displaystyle q} , over the same underlying...
19 KB (3,247 words) - 09:04, 20 October 2024
In information theory, the entropy of a random variable quantifies the average level of uncertainty or information associated with the variable's potential...
70 KB (10,018 words) - 00:46, 4 October 2024
The cross-entropy (CE) method is a Monte Carlo method for importance sampling and optimization. It is applicable to both combinatorial and continuous...
7 KB (1,082 words) - 14:36, 13 July 2024
Kullback–Leibler divergence (redirect from Kullback–Leibler entropy)
statistics, the Kullback–Leibler (KL) divergence (also called relative entropy and I-divergence), denoted D KL ( P ∥ Q ) {\displaystyle D_{\text{KL}}(P\parallel...
73 KB (12,534 words) - 13:54, 28 October 2024
Ensemble learning (section Amended Cross-Entropy Cost: An Approach for Encouraging Diversity in Classification Ensemble)
correlation for regression tasks or using information measures such as cross entropy for classification tasks. Theoretically, one can justify the diversity...
52 KB (6,574 words) - 06:47, 2 November 2024
entropy states that the probability distribution which best represents the current state of knowledge about a system is the one with largest entropy,...
31 KB (4,196 words) - 13:25, 2 November 2024
Perplexity (category Entropy and information)
{1}{N}}\sum _{i=1}^{N}\log _{b}q(x_{i})} may also be interpreted as a cross-entropy: H ( p ~ , q ) = − ∑ x p ~ ( x ) log b q ( x ) {\displaystyle H({\tilde...
13 KB (1,859 words) - 09:03, 20 October 2024
In physics, the Tsallis entropy is a generalization of the standard Boltzmann–Gibbs entropy. It is proportional to the expectation of the q-logarithm...
22 KB (2,563 words) - 17:47, 6 March 2024
Maximum likelihood estimation (section Relation to minimizing Kullback–Leibler divergence and cross entropy)
the relationship between maximizing the likelihood and minimizing the cross-entropy, URL (version: 2019-11-06): https://stats.stackexchange.com/q/364237...
67 KB (9,707 words) - 16:01, 1 November 2024
Neural machine translation (section Cross-entropy loss)
of the factors’ logarithms and flipping the sign yields the classic cross-entropy loss: θ ∗ = a r g m i n θ − ∑ i T log ∑ j = 1 J ( i ) P ( y j ( i...
36 KB (3,910 words) - 05:39, 9 October 2024