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» Results of the KDD'99 Classifier Learning
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ICML
2005
IEEE
14 years 8 months ago
Predicting relative performance of classifiers from samples
This paper is concerned with the problem of predicting relative performance of classification algorithms. It focusses on methods that use results on small samples and discusses th...
Rui Leite, Pavel Brazdil
CHI
2009
ACM
14 years 8 months ago
EnsembleMatrix: interactive visualization to support machine learning with multiple classifiers
Machine learning is an increasingly used computational tool within human-computer interaction research. While most researchers currently utilize an iterative approach to refining ...
Justin Talbot, Bongshin Lee, Ashish Kapoor, Desney...
CVPR
2001
IEEE
14 years 9 months ago
Bayesian Learning of Sparse Classifiers
Bayesian approaches to supervised learning use priors on the classifier parameters. However, few priors aim at achieving "sparse" classifiers, where irrelevant/redundant...
Anil K. Jain, Mário A. T. Figueiredo
CVPR
2008
IEEE
14 years 9 months ago
Boosting adaptive linear weak classifiers for online learning and tracking
Online boosting methods have recently been used successfully for tracking, background subtraction etc. Conventional online boosting algorithms emphasize on interchanging new weak ...
Toufiq Parag, Fatih Porikli, Ahmed M. Elgammal
GECCO
2008
Springer
128views Optimization» more  GECCO 2008»
13 years 8 months ago
Adapted Pittsburgh classifier system: building accurate strategies in non markovian environments
This paper focuses on the study of the behavior of a genetic algorithm based classifier system, the Adapted Pittsburgh Classifier System (A.P.C.S), on maze type environments con...
Gilles Énée, Mathias Péroumal...