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» Learning Taxonomies by Dependence Maximization
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ECML
2005
Springer
15 years 11 months ago
Fitting the Smallest Enclosing Bregman Ball
Finding a point which minimizes the maximal distortion with respect to a dataset is an important estimation problem that has recently received growing attentions in machine learnin...
Richard Nock, Frank Nielsen
GECCO
2005
Springer
132views Optimization» more  GECCO 2005»
15 years 11 months ago
A statistical learning theory approach of bloat
Code bloat, the excessive increase of code size, is an important issue in Genetic Programming (GP). This paper proposes a theoretical analysis of code bloat in the framework of sy...
Sylvain Gelly, Olivier Teytaud, Nicolas Bredeche, ...
NIPS
2007
15 years 7 months ago
Learning Horizontal Connections in a Sparse Coding Model of Natural Images
It has been shown that adapting a dictionary of basis functions to the statistics of natural images so as to maximize sparsity in the coefficients results in a set of dictionary ...
Pierre Garrigues, Bruno Olshausen
KR
1992
Springer
15 years 9 months ago
Learning Useful Horn Approximations
While the task of answering queries from an arbitrary propositional theory is intractable in general, it can typicallybe performed e ciently if the theory is Horn. This suggests t...
Russell Greiner, Dale Schuurmans
185
Voted
ML
2007
ACM
104views Machine Learning» more  ML 2007»
15 years 5 months ago
A general criterion and an algorithmic framework for learning in multi-agent systems
We offer a new formal criterion for agent-centric learning in multi-agent systems, that is, learning that maximizes one’s rewards in the presence of other agents who might also...
Rob Powers, Yoav Shoham, Thuc Vu