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A Memetic Algorithm for ILP Genetic Relational Search for Inductive Concept Learning.

Формат:      Страниц 192
     мягкая обложка
Federico D.    
Книга по Требованию    
2626 руб
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ECL relies on four greedy mutation operators for searching the hypothesis space, and employs an optimization phase that follows each mutation. When FOL hypotheses are used, this complexity is combined with the complexity of evaluating hypotheses. This problem is NP-hard even if the language to represent hypotheses is propositional logic. Experimental results show that ECL works well in practice. The algorithm, called ECL (for Evolutionary Concept Learner), evolves a population of Horn clauses by repeated selection, mutation and optimization of more fit clauses. Learning can be viewed as a search problem in the space of all possible hypotheses. This book describes an evolutionary algorithm for ILP. 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. 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.
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