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Author Opitz, David W. ♦ Shavlik, Jude W.
Source CiteSeerX
Content type Text
Publisher Morgan Kaufmann
File Format PDF
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Domain Theory ♦ Mutation Operator ♦ Genetic Algorithm ♦ Standard Connectionist Theory-refinement Sys ♦ New Algorithm ♦ Knowledge-based Neural Network ♦ Domainspecific Knowledge ♦ Realworld Domain ♦ Connectionist Theory-refinement System ♦ Genetic Search ♦ Domain-specific Knowledge ♦ Regent Algorithm ♦ Initial Population ♦ Ideal Inductive-learning Algorithm ♦ Available Resource
Description In Proceedings of the Eleventh International Conference on Machine Learning
An ideal inductive-learning algorithm should exploit all available resources, such as computing power and domain-specific knowledge, to improve its ability to generalize. Connectionist theory-refinement systems have proven to be effective at utilizing domainspecific knowledge; however, most are unable to exploit available computing power. This weakness occurs because they lack the ability to refine the topology of the networks they produce, thereby limiting generalization, especially when given impoverished domain theories. We present the Regent algorithm, which uses genetic algorithms to broaden the type of networks seen during its search. It does this by using (a) the domain theory to help create an initial population and (b) crossover and mutation operators specifically designed for knowledgebased networks. Experiments on three realworld domains indicate that our new algorithm is able to significantly increase generalization compared to a standard connectionist theory-refinement sys...
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 1994-01-01