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» Unsupervised feature selection using a neuro-fuzzy approach
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ECAI
2008
Springer
15 years 8 months ago
Reinforcement Learning with the Use of Costly Features
In many practical reinforcement learning problems, the state space is too large to permit an exact representation of the value function, much less the time required to compute it. ...
Robby Goetschalckx, Scott Sanner, Kurt Driessens
UM
2010
Springer
15 years 4 months ago
Inducing Effective Pedagogical Strategies Using Learning Context Features
Effective pedagogical strategies are important for e-learning environments. While it is assumed that an effective learning environment should craft and adapt its actions to the use...
Min Chi, Kurt VanLehn, Diane J. Litman, Pamela W. ...
ICIP
2003
IEEE
16 years 8 months ago
An ontology approach to object-based image retrieval
In this paper, an image retrieval methodology suited for search in large collections of heterogeneous images is presented. The proposed approach employs a fully unsupervised segme...
Vasileios Mezaris, Ioannis Kompatsiaris, Michael G...
PR
2008
93views more  PR 2008»
15 years 6 months ago
Genetic algorithm-based feature set partitioning for classification problems
Feature set partitioning generalizes the task of feature selection by partitioning the feature set into subsets of features that are collectively useful, rather than by finding a ...
Lior Rokach
CVPR
2001
IEEE
16 years 8 months ago
Rapid Object Detection using a Boosted Cascade of Simple Features
This paper describes a machine learning approach for visual object detection which is capable of processing images extremely rapidly and achieving high detection rates. This wor...
Paul A. Viola, Michael J. Jones