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FLAIRS
2003
15 years 6 months ago
Learning from Reinforcement and Advice Using Composite Reward Functions
1 Reinforcement learning has become a widely used methodology for creating intelligent agents in a wide range of applications. However, its performance deteriorates in tasks with s...
Vinay N. Papudesi, Manfred Huber
135
Voted
WECWIS
2009
IEEE
162views ECommerce» more  WECWIS 2009»
15 years 11 months ago
QoS-Driven Web Service Composition Using Learning-Based Depth First Search
—The goal of the Web Service Composition (WSC) problem is to find an optimal composition of web services to satisfy a given request using their syntactic and/or semantic feature...
Wonhong Nam, Hyunyoung Kil, Jungjae Lee
146
Voted
NPL
2002
151views more  NPL 2002»
15 years 4 months ago
Additive Composition of Supervised Self Organizing Maps
The learning of complex relationships can be decomposed into several neural networks. The modular organization is determined by prior knowledge of the problem that permits to split...
Jean-Luc Buessler, Jean-Philippe Urban, Julien Gre...
145
Voted
GECCO
2007
Springer
158views Optimization» more  GECCO 2007»
15 years 10 months ago
A novel generative encoding for exploiting neural network sensor and output geometry
A significant problem for evolving artificial neural networks is that the physical arrangement of sensors and effectors is invisible to the evolutionary algorithm. For example,...
David B. D'Ambrosio, Kenneth O. Stanley
COLT
2001
Springer
15 years 9 months ago
Geometric Bounds for Generalization in Boosting
We consider geometric conditions on a labeled data set which guarantee that boosting algorithms work well when linear classifiers are used as weak learners. We start by providing ...
Shie Mannor, Ron Meir