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GECCO
2009
Springer
188views Optimization» more  GECCO 2009»
13 years 11 months ago
Exploiting multiple classifier types with active learning
Many approaches to active learning involve training one classifier by periodically choosing new data points about which the classifier has the least confidence, but designing a co...
Zhenyu Lu, Josh Bongard
AMT
2006
Springer
107views Multimedia» more  AMT 2006»
13 years 11 months ago
An Intelligent Process Monitoring System in Complex Manufacturing Environment
In high-tech industries, most manufacturing processes are complexly intertwined, in that manufacturers or engineers can hardly control a whole set of processes. They are only capa...
Sung Ho Ha, Boo-Sik Kang
GECCO
2006
Springer
133views Optimization» more  GECCO 2006»
13 years 11 months ago
On-line evolutionary computation for reinforcement learning in stochastic domains
In reinforcement learning, an agent interacting with its environment strives to learn a policy that specifies, for each state it may encounter, what action to take. Evolutionary c...
Shimon Whiteson, Peter Stone
CIBCB
2009
IEEE
13 years 9 months ago
Application of machine learning approaches on quantitative structure activity relationships
Machine Learning techniques are successfully applied to establish quantitative relations between chemical structure and biological activity (QSAR), i.e. classify compounds as activ...
Mariusz Butkiewicz, Ralf Mueller, Danilo Selic, Er...
ASC
2010
13 years 8 months ago
Simplifying Particle Swarm Optimization
The general purpose optimization method known as Particle Swarm Optimization (PSO) has received much attention in past years, with many attempts to find the variant that performs ...
M. E. H. Pedersen, Andrew J. Chipperfield