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Generalizing over Several Learning Settings

Identifieur interne : 000C10 ( Istex/Corpus ); précédent : 000C09; suivant : 000C11

Generalizing over Several Learning Settings

Source :

Lecture Notes in Computer Science [ 0302-9743 ] ; 2010.

RBID : ISTEX:76C981C39E161243487E92ECE2960115A0240FA2

Abstract

Abstract: We recapitulate inference from membership and equivalence queries, positive and negative samples. Regular languages cannot be learned from one of those information sources only [1,2,3]. Combinations of two sources allowing regular (polynomial) inference are MQs and EQs [4], MQs and positive data [5,6], positive and negative data [7,8]. We sketch a meta-algorithm fully presented in [9] that generalizes over as many combinations of those sources as possible. This includes a survey of pairings for which there are no well-studied algorithms.

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https://api.istex.fr/document/76C981C39E161243487E92ECE2960115A0240FA2/fulltext/pdf

DOI: 10.1007/978-3-642-15488-1_28

Links to Exploration step

ISTEX:76C981C39E161243487E92ECE2960115A0240FA2

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<abstract lang="en">Abstract: We recapitulate inference from membership and equivalence queries, positive and negative samples. Regular languages cannot be learned from one of those information sources only [1,2,3]. Combinations of two sources allowing regular (polynomial) inference are MQs and EQs [4], MQs and positive data [5,6], positive and negative data [7,8]. We sketch a meta-algorithm fully presented in [9] that generalizes over as many combinations of those sources as possible. This includes a survey of pairings for which there are no well-studied algorithms.</abstract>
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Generalizing over Several Learning Settings

Generalizing over Several Learning Settings

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