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CG
2000
Springer
15 years 2 months ago
Chess Neighborhoods, Function Combination, and Reinforcement Learning
Abstract. Over the years, various research projects have attempted to develop a chess program that learns to play well given little prior knowledge beyond the rules of the game. Ea...
Robert Levinson, Ryan Weber
ML
2007
ACM
144views Machine Learning» more  ML 2007»
14 years 9 months ago
Invariant kernel functions for pattern analysis and machine learning
In many learning problems prior knowledge about pattern variations can be formalized and beneficially incorporated into the analysis system. The corresponding notion of invarianc...
Bernard Haasdonk, Hans Burkhardt
BMCBI
2006
72views more  BMCBI 2006»
14 years 10 months ago
Selecting effective siRNA sequences by using radial basis function network and decision tree learning
Background: Although short interfering RNA (siRNA) has been widely used for studying gene functions in mammalian cells, its gene silencing efficacy varies markedly and there are o...
Shigeru Takasaki, Yoshihiro Kawamura, Akihiko Kona...
GECCO
2003
Springer
114views Optimization» more  GECCO 2003»
15 years 3 months ago
Learning the Ideal Evaluation Function
Abstract. Designing an adequate fitness function requiressubstantial knowledge of a problem and of features that indicate progress towards a solution. Coevolution takes the human ...
Edwin D. de Jong, Jordan B. Pollack
ICDM
2005
IEEE
116views Data Mining» more  ICDM 2005»
15 years 3 months ago
Learning Functional Dependency Networks Based on Genetic Programming
Bayesian Network (BN) is a powerful network model, which represents a set of variables in the domain and provides the probabilistic relationships among them. But BN can handle dis...
Wing-Ho Shum, Kwong-Sak Leung, Man Leung Wong