About: Distribution learning theory

Facets (new session)
Description
Metadata
Settings
- Rule:
- Inverse Functional Properties:
- "Same As":

About: Distribution learning theory Goto Sponge NotDistinct Permalink

An Entity of Type : dbo:Software, within Data Space : dbpedia.demo.openlinksw.com associated with source document(s)
QRcode icon

http://dbpedia.demo.openlinksw.com/describe/?url=http%3A%2F%2Fdbpedia.org%2Fresource%2FDistribution_learning_theory

The distributional learning theory or learning of probability distribution is a framework in computational learning theory. It has been proposed from Michael Kearns, , Dana Ron, Ronitt Rubinfeld, Robert Schapire and in 1994 and it was inspired from the PAC-framework introduced by Leslie Valiant. This article explains the basic definitions, tools and results in this framework from the theory of computation point of view.

Attributes	Values
rdf:type	software
rdfs:label	Distribution learning theory (en)
rdfs:comment	The distributional learning theory or learning of probability distribution is a framework in computational learning theory. It has been proposed from Michael Kearns, , Dana Ron, Ronitt Rubinfeld, Robert Schapire and in 1994 and it was inspired from the PAC-framework introduced by Leslie Valiant. This article explains the basic definitions, tools and results in this framework from the theory of computation point of view. (en)
dcterms:subject	Computational learning theory
Wikipage page ID	44655565 (xsd:integer)
Wikipage revision ID	1083045386 (xsd:integer)
Link from a Wikipage to another Wikipage	Probability distribution Robert Schapire Michael Kearns (computer scientist) Computational learning theory Applied probability Constantinos Daskalakis Conditional probability distribution Dana Ron Approximation algorithms Leslie Valiant Machine learning Statistics Cluster analysis Computational learning theory Kolmogorov–Smirnov test Kullback-Leibler divergence Ronitt Rubinfeld Total variation Computational learning theory Total variation distance of probability measures PAC-learning dbr:Gautam_Kamath dbr:Linda_Sellie dbr:S._Dasgupta dbr:Yishay_Mansour
sameAs	Distribution learning theory Distribution learning theory Distribution learning theory
has abstract	The distributional learning theory or learning of probability distribution is a framework in computational learning theory. It has been proposed from Michael Kearns, , Dana Ron, Ronitt Rubinfeld, Robert Schapire and 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 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. (en)
gold:hypernym	Framework
prov:wasDerivedFrom	wikipedia-en:Distribution_learning_theory?oldid=1083045386&ns=0
page length (characters) of wiki page	22325 (xsd:nonNegativeInteger)
foaf:isPrimaryTopicOf	wikipedia-en:Distribution_learning_theory
is Link from a Wikipage to another Wikipage of	Computational learning theory Adam Tauman Kalai Outline of machine learning
is foaf:primaryTopic of	wikipedia-en:Distribution_learning_theory

Faceted Search & Find service v1.17_git139 as of Feb 29 2024

Alternative Linked Data Documents: ODE Content Formats:

RDF

ODATA

Microdata

About

OpenLink Virtuoso version 08.03.3330 as of Mar 19 2024, on Linux (x86_64-generic-linux-glibc212), Single-Server Edition (378 GB total memory, 59 GB memory in use)
Data on this page belongs to its respective rights holders.
Virtuoso Faceted Browser Copyright © 2009-2024 OpenLink Software