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» Learning to generalize for complex selection tasks
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CVPR
2006
IEEE
14 years 10 months ago
Hierarchical Statistical Learning of Generic Parts of Object Structure
With the growing interest in object categorization various methods have emerged that perform well in this challenging task, yet are inherently limited to only a moderate number of...
Sanja Fidler, Gregor Berginc, Ales Leonardis
MIR
2004
ACM
171views Multimedia» more  MIR 2004»
14 years 1 months ago
Mean version space: a new active learning method for content-based image retrieval
In content-based image retrieval, relevance feedback has been introduced to narrow the gap between low-level image feature and high-level semantic concept. Furthermore, to speed u...
Jingrui He, Hanghang Tong, Mingjing Li, HongJiang ...
GECCO
2005
Springer
153views Optimization» more  GECCO 2005»
14 years 2 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
AI
2002
Springer
13 years 8 months ago
Improving heuristic mini-max search by supervised learning
This article surveys three techniques for enhancing heuristic game-tree search pioneered in the author's Othello program Logistello, which dominated the computer Othello scen...
Michael Buro
ICML
2009
IEEE
14 years 9 months ago
Large-scale deep unsupervised learning using graphics processors
The promise of unsupervised learning methods lies in their potential to use vast amounts of unlabeled data to learn complex, highly nonlinear models with millions of free paramete...
Rajat Raina, Anand Madhavan, Andrew Y. Ng