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» Evaluating learning algorithms and classifiers
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GECCO
2006
Springer
214views Optimization» more  GECCO 2006»
15 years 8 months ago
A new discrete particle swarm algorithm applied to attribute selection in a bioinformatics data set
Many data mining applications involve the task of building a model for predictive classification. The goal of such a model is to classify examples (records or data instances) into...
Elon S. Correa, Alex Alves Freitas, Colin G. Johns...
ECCV
2008
Springer
15 years 6 months ago
Vision-Based Multiple Interacting Targets Tracking via On-Line Supervised Learning
Abstract. Successful multi-target tracking requires locating the targets and labeling their identities. This mission becomes significantly more challenging when many targets freque...
Xuan Song, Jinshi Cui, Hongbin Zha, Huijing Zhao
FLAIRS
2004
15 years 6 months ago
Gene Expression Data Classification with Revised Kernel Partial Least Squares Algorithm
One important feature of the gene expression data is that the number of genes M far exceeds the number of samples N. Standard statistical methods do not work well when N < M. D...
ZhenQiu Liu, Dechang Chen
CVPR
2007
IEEE
16 years 6 months ago
Learning Features for Tracking
We treat tracking as a matching problem of detected keypoints between successive frames. The novelty of this paper is to learn classifier-based keypoint descriptions allowing to i...
Michael Grabner, Helmut Grabner, Horst Bischof
ECAI
2004
Springer
15 years 10 months ago
Learning Techniques for Automatic Algorithm Portfolio Selection
The purpose of this paper is to show that a well known machine learning technique based on Decision Trees can be effectively used to select the best approach (in terms of efficien...
Alessio Guerri, Michela Milano