About: Error tolerance (PAC learning)

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In PAC learning, error tolerance refers to the ability of an algorithm to learn when the examples received have been corrupted in some way. In fact, this is a very common and important issue since in many applications it is not possible to access noise-free data. Noise can interfere with the learning process at different levels: the algorithm may receive data that have been occasionally mislabeled, or the inputs may have some false information, or the classification of the examples may have been maliciously adulterated.

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rdfs:label	Error tolerance (PAC learning) (en)
rdfs:comment	In PAC learning, error tolerance refers to the ability of an algorithm to learn when the examples received have been corrupted in some way. In fact, this is a very common and important issue since in many applications it is not possible to access noise-free data. Noise can interfere with the learning process at different levels: the algorithm may receive data that have been occasionally mislabeled, or the inputs may have some false information, or the classification of the examples may have been maliciously adulterated. (en)
dbp:wikiPageUsesTemplate	dbt:Columns-list dbt:Reflist dbt:Use_dmy_dates dbt:Machine_learning_bar
Subject	Computational learning theory Theoretical computer science Machine learning
prov:wasDerivedFrom	http://en.wikipedia.org/wiki/Error_tolerance_(PAC_learning)?oldid=1023213519&ns=0
Wikipage page ID	48833041 (xsd:integer)
page length (characters) of wiki page	11435 (xsd:nonNegativeInteger)
Wikipage revision ID	1023213519 (xsd:integer)
Link from a Wikipage to another Wikipage	Machine learning Probably approximately correct learning Active learning (machine learning) Adversarial machine learning Algorithm Boolean function Burst error Computational learning theory Theoretical computer science Machine learning Parity (mathematics) Data mining Leslie Valiant
has abstract	In PAC learning, error tolerance refers to the ability of an algorithm to learn when the examples received have been corrupted in some way. In fact, this is a very common and important issue since in many applications it is not possible to access noise-free data. Noise can interfere with the learning process at different levels: the algorithm may receive data that have been occasionally mislabeled, or the inputs may have some false information, or the classification of the examples may have been maliciously adulterated. (en)
foaf:isPrimaryTopicOf	http://en.wikipedia.org/wiki/Error_tolerance_(PAC_learning)
is Wikipage redirect of	Error Tolerance (PAC learning)
is Link from a Wikipage to another Wikipage of	Statistical learning theory Error Tolerance (PAC learning)
is foaf:primaryTopic of	http://en.wikipedia.org/wiki/Error_tolerance_(PAC_learning)

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