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PLILP
1993
Springer
14 years 1 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...
GECCO
2004
Springer
122views Optimization» more  GECCO 2004»
14 years 2 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
EMMCVPR
2005
Springer
14 years 2 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...
ICML
2000
IEEE
14 years 9 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
NIPS
1998
13 years 10 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