Using a priori knowledge to create probabilistic models for optimization.

DOI10.1016/S0888-613X(02)00094-4zbMath1056.68065OpenAlexW2089406322MaRDI QIDQ1399502

Shumeet Baluja

Publication date: 30 July 2003

Published in: International Journal of Approximate Reasoning (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/s0888-613x(02)00094-4

zbMATH Keywords

searchingparameter interdependencies

Mathematics Subject Classification ID

Searching and sorting (68P10) Problem solving in the context of artificial intelligence (heuristics, search strategies, etc.) (68T20)

Related Items (2)

Online optimization of replacement policies using learning automata ⋮ Learning weighted linguistic fuzzy rules by using specifically-tailored hybrid estimation of distribution algorithms

Cites Work

• Schemata, distributions and graphical models in evolutionary optimization
• Learning Bayesian networks: The combination of knowledge and statistical data
• Learning Optimal Discriminant Functions through a Cooperative Game of Automata
• Approximating discrete probability distributions with dependence trees
• A survey of optimization by building and using probabilistic models
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