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» Genetic Approach for Optimizing Ensembles of Classifiers
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GECCO
2005
Springer
153views Optimization» more  GECCO 2005»
14 years 1 months ago
Evolving neural network ensembles for control problems
In neuroevolution, a genetic algorithm is used to evolve a neural network to perform a particular task. The standard approach is to evolve a population over a number of generation...
David Pardoe, Michael S. Ryoo, Risto Miikkulainen
GECCO
2008
Springer
135views Optimization» more  GECCO 2008»
13 years 9 months ago
Evolving sequence patterns for prediction of sub-cellular locations of eukaryotic proteins
A genetic algorithm (GA) is utilised to discover known and novel PROSITE-like sequence templates that can be used to classify the sub-cellular location of eukaryotic proteins. Whi...
Greg Paperin
ISMIR
2005
Springer
196views Music» more  ISMIR 2005»
14 years 1 months ago
ACE: A Framework for Optimizing Music Classification
This paper presents ACE (Autonomous Classification Engine), a framework for using and optimizing classifiers. Given a set of feature vectors, ACE experiments with a variety of cla...
Cory McKay, Rebecca Fiebrink, Daniel McEnnis, Bein...
MCS
2005
Springer
14 years 1 months ago
Ensemble Confidence Estimates Posterior Probability
We have previously introduced the Learn++ algorithm that provides surprisingly promising performance for incremental learning as well as data fusion applications. In this contribut...
Michael Muhlbaier, Apostolos Topalis, Robi Polikar
EUROGP
2007
Springer
126views Optimization» more  EUROGP 2007»
13 years 11 months ago
Training Binary GP Classifiers Efficiently: A Pareto-coevolutionary Approach
The conversion and extension of the Incremental Pareto-Coevolution Archive algorithm (IPCA) into the domain of Genetic Programming classification is presented. In particular, the ...
Michal Lemczyk, Malcolm I. Heywood