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GECCO
2004
Springer
103views Optimization» more  GECCO 2004»
15 years 10 months ago
Training Neural Networks with GA Hybrid Algorithms
Abstract. Training neural networks is a complex task of great importance in the supervised learning field of research. In this work we tackle this problem with five algorithms, a...
Enrique Alba, J. Francisco Chicano
157
Voted
CEC
2009
IEEE
15 years 9 months ago
Memory-enhanced Evolutionary Robotics: The Echo State Network Approach
— Interested in Evolutionary Robotics, this paper focuses on the acquisition and exploitation of memory skills. The targeted task is a well-studied benchmark problem, the Tolman ...
Cédric Hartland, Nicolas Bredeche, Mich&egr...
ICPR
2000
IEEE
15 years 9 months ago
Feature Learning for Recognition with Bayesian Networks
Many realistic visual recognition tasks are “open” in the sense that the number and nature of the categories to be learned are not initially known, and there is no closed set ...
Justus H. Piater, Roderic A. Grupen
IPPS
1998
IEEE
15 years 9 months ago
Memory Space Representation for Heterogeneous Network Process Migration
A major difficulty of heterogeneous process migration is how to collect advanced dynamic data-structures, transform them into machine independent form, and restor them appropriate...
Kasidit Chanchio, Xian-He Sun
194
Voted
ACIVS
2006
Springer
15 years 8 months ago
Context-Based Scene Recognition Using Bayesian Networks with Scale-Invariant Feature Transform
Scene understanding is an important problem in intelligent robotics. Since visual information is uncertain due to several reasons, we need a novel method that has robustness to the...
Seung-Bin Im, Sung-Bae Cho