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Genetic Relational Search for Inductive Concept Learning   Federico Divina

Genetic Relational Search for Inductive Concept Learning

192 страниц. 2010 год.
LAP Lambert Academic Publishing
Learning from examples in First Order Logic, also known as Inductive Logic Programming (ILP), constitutes a central topic in Machine Learning, with relevant applications to problems in complex domains, e.g., natural language and computational biology. Learning can be viewed as a search problem in the space of all possible hypotheses. Given a background knowledge, a set of positive examples and a set of negative examples, expressed in First Order Logic, one has to find a hypothesis which covers all positive examples and none of the negative ones. This problem is NP-hard even if the language to represent hypotheses is propositional logic. When FOL hypotheses are used, this complexity is combined with the complexity of evaluating hypotheses. This book describes an evolutionary algorithm for ILP. The algorithm, called ECL (for Evolutionary Concept Learner), evolves a population of Horn clauses by repeated selection, mutation and optimization of more fit clauses. ECL relies on...
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