Distribution learning theory
id:
distribution-learning-theory-244-1733749
title:
Distribution learning theory
text:
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,
brand slug:
wiki
category slug:
encyclopedia
description:
original url:
https://en.wikipedia.org/wiki/Distribution_learning_theory
date created:
date modified:
2022-04-16T17:38:05Z
main entity:
{"identifier":"Q25048711","url":"https://www.wikidata.org/entity/Q25048711"}
image:
fields total:
13
integrity:
13