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» Algorithm Selection using Reinforcement Learning
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EC
2006
121views ECommerce» more  EC 2006»
15 years 2 months ago
A Study of Structural and Parametric Learning in XCS
The performance of a learning classifier system is due to its two main components. First, it evolves new structures by generating new rules in a genetic process; second, it adjust...
Tim Kovacs, Manfred Kerber
113
Voted
SDM
2009
SIAM
154views Data Mining» more  SDM 2009»
15 years 11 months ago
AMORI: A Metric-Based One Rule Inducer.
The requirements of real-world data mining problems vary extensively. It is plausible to assume that some of these requirements can be expressed as application-specific performan...
Niklas Lavesson, Paul Davidsson
GECCO
2006
Springer
173views Optimization» more  GECCO 2006»
15 years 6 months ago
Pareto-coevolutionary genetic programming classifier
The conversion and extension of the Incremental ParetoCoevolution Archive algorithm (IPCA) into the domain of Genetic Programming classifier evolution is presented. In order to ac...
Michal Lemczyk, Malcolm I. Heywood
128
Voted
SIGCSE
2005
ACM
146views Education» more  SIGCSE 2005»
15 years 8 months ago
Using image processing projects to teach CS1 topics
As Computer Science educators, we know that students learn more from projects that are fun and challenging, that seem “real” to them, and that allow them to be creative in des...
Richard Wicentowski, Tia Newhall
148
Voted
SDM
2012
SIAM
216views Data Mining» more  SDM 2012»
13 years 5 months ago
Feature Selection "Tomography" - Illustrating that Optimal Feature Filtering is Hopelessly Ungeneralizable
:  Feature Selection “Tomography” - Illustrating that Optimal Feature Filtering is Hopelessly Ungeneralizable George Forman HP Laboratories HPL-2010-19R1 Feature selection; ...
George Forman