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133
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PLILP
1993
Springer
15 years 7 months ago
Narrowing Approximations as an Optimization for Equational Logic Programs
Abstract. Solving equations in equational theories is a relevant programming paradigm which integrates logic and equational programming into one unified framework. Efficient metho...
María Alpuente, Moreno Falaschi, Marí...
136
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GECCO
2004
Springer
122views Optimization» more  GECCO 2004»
15 years 8 months ago
Gradient-Based Learning Updates Improve XCS Performance in Multistep Problems
This paper introduces a gradient-based reward prediction update mechanism to the XCS classifier system as applied in neuralnetwork type learning and function approximation mechani...
Martin V. Butz, David E. Goldberg, Pier Luca Lanzi
146
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EMMCVPR
2005
Springer
15 years 9 months ago
Optimizing the Cauchy-Schwarz PDF Distance for Information Theoretic, Non-parametric Clustering
This paper addresses the problem of efficient information theoretic, non-parametric data clustering. We develop a procedure for adapting the cluster memberships of the data pattern...
Robert Jenssen, Deniz Erdogmus, Kenneth E. Hild II...
131
Voted
ICML
2000
IEEE
16 years 4 months ago
Rates of Convergence for Variable Resolution Schemes in Optimal Control
This paper presents a general method to derive tight rates of convergence for numerical approximations in optimal control when we consider variable resolution grids. We study the ...
Andrew W. Moore, Rémi Munos
139
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NIPS
1998
15 years 4 months ago
Gradient Descent for General Reinforcement Learning
A simple learning rule is derived, the VAPS algorithm, which can be instantiated to generate a wide range of new reinforcementlearning algorithms. These algorithms solve a number ...
Leemon C. Baird III, Andrew W. Moore