Results 31 to 40 of about 255,284 (162)
Model Averaging Estimation Method by Kullback–Leibler Divergence for Multiplicative Error Model
Complexity, 2022 In this paper, we propose the model averaging estimation method for multiplicative error model and construct the corresponding weight choosing criterion based on the Kullback–Leibler divergence with a hyperparameter to avoid the problem of overfitting ...
Wanbo Lu, Wenhui Shi
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On accuracy of PDF divergence estimators and their applicability to representative data sampling [PDF]
, 2011 Generalisation error estimation is an important issue in machine learning. Cross-validation traditionally used for this purpose requires building multiple models and repeating the whole procedure many times in order to produce reliable error estimates ...
Musial, Katarzyna +6 more
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Rényi Relative Entropy from Homogeneous Kullback-Leibler Divergence Lagrangian [PDF]
, 2021 We study the homogeneous extension of the Kullback-Leibler divergence associated to a covariant variational problem on the statistical bundle. We assume a finite sample space.
Goffredo Chirco, Chirco G.
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Entropy, 2016 Information-theoretic measures, such as the entropy, the cross-entropy and the Kullback–Leibler divergence between two mixture models, are core primitives in many signal processing tasks.
Frank Nielsen, Ke Sun
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Some bounds for skewed α-Jensen-Shannon divergence
Results in Applied Mathematics, 2019 Based on the skewed Kullback-Leibler divergence introduced in the natural language processing, we derive the upper and lower bounds on the skewed version of the Jensen-Shannon divergence and investigate properties of them.
Takuya Yamano
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Model Fusion with Kullback--Leibler Divergence
CoRR, 2020 We propose a method to fuse posterior distributions learned from heterogeneous datasets. Our algorithm relies on a mean field assumption for both the fused model and the individual dataset posteriors and proceeds using a simple assign-and-average approach.
Sebastian Claici +3 more
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Interpreting Kullback-Leibler divergence with the Neyman-Pearson lemma [PDF]
, 2006 Kullback-Leibler divergence and the Neyman-Pearson lemma are two fundamental concepts in statistics. Both are about likelihood ratios: Kullback-Leibler divergence is the expected log-likelihood ratio, and the Neyman-Pearson lemma is about error rates of ...
Copas, John B. +2 more
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Dynamic fine‐tuning layer selection using Kullback–Leibler divergence
Engineering Reports, 2023 The selection of layers in the transfer learning fine‐tuning process ensures a pre‐trained model's accuracy and adaptation in a new target domain. However, the selection process is still manual and without clearly defined criteria. If the wrong layers in
Raphael Ngigi Wanjiku +2 more
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Algorithms, 2022 The family of α-divergences including the oriented forward and reverse Kullback–Leibler divergences is often used in signal processing, pattern recognition, and machine learning, among others.
Frank Nielsen
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Optimism in reinforcement learning and Kullback-Leibler divergence [PDF]
2010 48th Annual Allerton Conference on Communication, Control, and Computing (Allerton), 2010 We consider model-based reinforcement learning in finite Markov De- cision Processes (MDPs), focussing on so-called optimistic strategies. In MDPs, optimism can be implemented by carrying out extended value it- erations under a constraint of consistency with the estimated model tran- sition probabilities. The UCRL2 algorithm by Auer, Jaksch and Ortner (
Sarah Filippi +2 more
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