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Author Raedt, Luc De ♦ Laer, Wim Van
Source CiteSeerX
Content type Text
Publisher Springer-Verlag
File Format PDF
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Full First Order Formula ♦ Positive Example ♦ Representation Duality ♦ Novel Approach ♦ Concept Representation ♦ System Employ Example ♦ Special Case ♦ Inductive Logic ♦ Inductive Constraint Logic ♦ Cn2 Type Algorithm ♦ Whereas Present Inductive Logic ♦ Disjunctive Normal Form ♦ Whereas Classical Learning Technique ♦ Clausal Representation ♦ Negative Example ♦ First Order Logic Formula ♦ Target Theory ♦ False Ground Fact ♦ Conjuctive Normal Form ♦ Classical Attribute Value
Description . A novel approach to learning first order logic formulae from positive and negative examples is presented. Whereas present inductive logic programming systems employ examples as true and false ground facts (or clauses), we view examples as interpretations which are true or false for the target theory. This viewpoint allows to reconcile the inductive logic programming paradigm with classical attribute value learning in the sense that the latter is a special case of the former. Because of this property, we are able to adapt AQ and CN2 type algorithms in order to enable learning of full first order formulae. However, whereas classical learning techniques have concentrated on concept representations in disjunctive normal form, we will use a clausal representation, which corresponds to a conjuctive normal form where each conjunct forms a constraint on positive examples. This representation duality reverses also the role of positive and negative examples, both in the heuristics and in the a...
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research
Education Level UG and PG ♦ Career/Technical Study
Learning Resource Type Article
Publisher Date 1995-01-01