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Kullback–Leibler divergence

Known as: Kl-divergence, KL-distance, Kullback divergence 
In probability theory and information theory, the Kullback–Leibler divergence, also called discrimination information (the name preferred by Kullback… Expand
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Related topics

Related topics

50 relations
AutoencoderBayesian information criterionBiclusteringBoltzmann machine
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Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
Review
2018
Review
2018
On the Feature Selection and Classification Based on Information Gain for Document Sentiment Analysis
Sentiment analysis in a movie review is the needs of today lifestyle. Unfortunately, enormous features make the sentiment of… Expand
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Review
2017
Review
2017
Incidental exposure to news: Predictors in the social media setting and effects on information gain online
Abstract Employing two studies, this paper investigates incidental exposure to news online in terms of its influence on… Expand
Highly Cited
2012
Highly Cited
2012
Kullback-Leibler divergence constrained distributionally robust optimization
In this paper we study distributionally robust optimization (DRO) problems where the ambiguity set of the probability… Expand
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Highly Cited
2011
Highly Cited
2011
Algorithms for Nonnegative Matrix Factorization with the β-Divergence
This letter describes algorithms for nonnegative matrix factorization (NMF) with the β-divergence (β-NMF). The β-divergence is a… Expand
Highly Cited
2011
Highly Cited
2011
Kullback-Leibler Divergence
  • James M. Joyce
  • International Encyclopedia of Statistical Science
  • 2011
    • Corpus ID: 37718089
 
Highly Cited
2010
Highly Cited
2010
Optimism in reinforcement learning and Kullback-Leibler divergence
We consider model-based reinforcement learning in finite Markov Decision Processes (MDPs), focussing on so-called optimistic… Expand
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Highly Cited
2008
Highly Cited
2008
Kullback-Leibler divergence estimation of continuous distributions
  • F. Pérez-Cruz
  • IEEE International Symposium on Information…
  • 2008
    • Corpus ID: 8811865
We present a method for estimating the KL divergence between continuous densities and we prove it converges almost surely… Expand
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Highly Cited
2007
Highly Cited
2007
Approximating the Kullback Leibler Divergence Between Gaussian Mixture Models
The Kullback Leibler (KL) divergence is a widely used tool in statistics and pattern recognition. The KL divergence between two… Expand
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Highly Cited
2005
Highly Cited
2005
Divergence measures and message passing
This paper presents a unifying view of messagepassing algorithms, as methods to approximate a complex Bayesian network by a… Expand
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Highly Cited
2003
Highly Cited
2003
A Kullback-Leibler Divergence Based Kernel for SVM Classification in Multimedia Applications
Over the last years significant efforts have been made to develop kernels that can be applied to sequence data such as DNA, text… Expand
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