Generalizing over Several Learning Settings
Identifieur interne : 000B78 ( Main/Exploration ); précédent : 000B77; suivant : 000B79Generalizing over Several Learning Settings
Auteurs : Anna Kasprzik [Allemagne]Source :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 2010.
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.
Url:
DOI: 10.1007/978-3-642-15488-1_28
Affiliations:
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Le document en format XML
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<front><div type="abstract" xml: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.</div>
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