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ECAI
2006
Springer
13 years 8 months ago
Learning by Automatic Option Discovery from Conditionally Terminating Sequences
Abstract. This paper proposes a novel approach to discover options in the form of conditionally terminating sequences, and shows how they can be integrated into reinforcement learn...
Sertan Girgin, Faruk Polat, Reda Alhajj
FLAIRS
2004
13 years 6 months ago
State Space Reduction For Hierarchical Reinforcement Learning
er provides new techniques for abstracting the state space of a Markov Decision Process (MDP). These techniques extend one of the recent minimization models, known as -reduction, ...
Mehran Asadi, Manfred Huber
ICPR
2008
IEEE
14 years 6 months ago
Feature Fusion Hierarchies for gender classification
We present a hierarchical feature fusion model for image classification that is constructed by an evolutionary learning algorithm. The model has the ability to combine local patch...
Fabien Scalzo, George Bebis, Mircea Nicolescu, Lea...
EMMCVPR
2005
Springer
13 years 10 months ago
Learning Hierarchical Shape Models from Examples
Abstract. We present an algorithm for automatically constructing a decompositional shape model from examples. Unlike current approaches to structural model acquisition, in which on...
Alex Levinshtein, Cristian Sminchisescu, Sven J. D...
PR
2007
118views more  PR 2007»
13 years 4 months ago
A cooperative constructive method for neural networks for pattern recognition
In this paper, we propose a new constructive method, based on cooperative coevolution, for designing automatically the structure of a neural network for classification. Our appro...
Nicolás García-Pedrajas, Domingo Ort...