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- Distribution_learning_theory abstract "The distributional learning theory or learning of probability distribution is a framework in computational learning theory. It has been proposed from Michael Kearns, Yishay Mansour, Dana Ron, Ronitt Rubinfeld, Robert Schapire and Linda Sellie in 1994 and it was inspired from the PAC-framework introduced by Leslie Valiant.In this framework the input is a number of samples drawn from a distribution that belongs to a specific class of distributions. The goal is to find an efficient algorithm that, based on these samples, determines with high probability the distribution from which the samples have been drawn. Because of its generality this framework it has been used in a large variety of different fields like machine learning, approximation algorithms, applied probability and statistics.This article explains the basic definitions, tools and results in this framework from the theory of computation point of view.".
- Distribution_learning_theory wikiPageID "44655565".
- Distribution_learning_theory wikiPageLength "22428".
- Distribution_learning_theory wikiPageOutDegree "29".
- Distribution_learning_theory wikiPageRevisionID "694617565".
- Distribution_learning_theory wikiPageWikiLink Applied_probability.
- Distribution_learning_theory wikiPageWikiLink Approximation_algorithm.
- Distribution_learning_theory wikiPageWikiLink Category:Computational_learning_theory.
- Distribution_learning_theory wikiPageWikiLink Cluster_analysis.
- Distribution_learning_theory wikiPageWikiLink Computational_learning_theory.
- Distribution_learning_theory wikiPageWikiLink Conditional_probability_distribution.
- Distribution_learning_theory wikiPageWikiLink Constantinos_Daskalakis.
- Distribution_learning_theory wikiPageWikiLink Dana_Ron.
- Distribution_learning_theory wikiPageWikiLink Gautam_Kamath.
- Distribution_learning_theory wikiPageWikiLink Kolmogorov–Smirnov_test.
- Distribution_learning_theory wikiPageWikiLink Kullback–Leibler_divergence.
- Distribution_learning_theory wikiPageWikiLink Leslie_Valiant.
- Distribution_learning_theory wikiPageWikiLink Linda_Sellie.
- Distribution_learning_theory wikiPageWikiLink Machine_learning.
- Distribution_learning_theory wikiPageWikiLink Michael_Kearns_(computer_scientist).
- Distribution_learning_theory wikiPageWikiLink Probability_distribution.
- Distribution_learning_theory wikiPageWikiLink Probably_approximately_correct_learning.
- Distribution_learning_theory wikiPageWikiLink Robert_Schapire.
- Distribution_learning_theory wikiPageWikiLink Ronitt_Rubinfeld.
- Distribution_learning_theory wikiPageWikiLink S._Dasgupta.
- Distribution_learning_theory wikiPageWikiLink Statistical_learning_theory.
- Distribution_learning_theory wikiPageWikiLink Statistics.
- Distribution_learning_theory wikiPageWikiLink Total_variation.
- Distribution_learning_theory wikiPageWikiLink Yishay_Mansour.
- Distribution_learning_theory wikiPageWikiLinkText "Distribution Learning Theory".
- Distribution_learning_theory subject Category:Computational_learning_theory.
- Distribution_learning_theory hypernym Framework.
- Distribution_learning_theory type Software.
- Distribution_learning_theory comment "The distributional learning theory or learning of probability distribution is a framework in computational learning theory. It has been proposed from Michael Kearns, Yishay Mansour, Dana Ron, Ronitt Rubinfeld, Robert Schapire and Linda Sellie in 1994 and it was inspired from the PAC-framework introduced by Leslie Valiant.In this framework the input is a number of samples drawn from a distribution that belongs to a specific class of distributions.".
- Distribution_learning_theory label "Distribution learning theory".
- Distribution_learning_theory sameAs m.012gc144.
- Distribution_learning_theory wasDerivedFrom Distribution_learning_theory?oldid=694617565.
- Distribution_learning_theory isPrimaryTopicOf Distribution_learning_theory.