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» Pruning Training Sets for Learning of Object Categories
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SIGIR
2006
ACM
14 years 2 months ago
Large scale semi-supervised linear SVMs
Large scale learning is often realistic only in a semi-supervised setting where a small set of labeled examples is available together with a large collection of unlabeled data. In...
Vikas Sindhwani, S. Sathiya Keerthi
GECCO
2007
Springer
167views Optimization» more  GECCO 2007»
14 years 3 months ago
Genetically designed heuristics for the bin packing problem
The bin packing problem (BPP) is a real-world problem that arises in different industrial applications related to minimization of space or time. The aim of this research is to au...
Oana Muntean
COMPGEOM
2011
ACM
13 years 16 days ago
Comparing distributions and shapes using the kernel distance
Starting with a similarity function between objects, it is possible to define a distance metric (the kernel distance) on pairs of objects, and more generally on probability distr...
Sarang C. Joshi, Raj Varma Kommaraju, Jeff M. Phil...
HYBRID
1998
Springer
14 years 1 months ago
A Connectionist Simulation of the Empirical Acquisition of Grammatical Relations
Abstract. This paper proposes an account of the acquisition of grammatical relations using the basic concepts of connectionism and a construction-based theory of grammar. Many prev...
William C. Morris, Garrison W. Cottrell, Jeffrey L...
CVPR
2007
IEEE
14 years 11 months ago
Region Classification with Markov Field Aspect Models
Considerable advances have been made in learning to recognize and localize visual object classes. Simple bag-offeature approaches label each pixel or patch independently. More adv...
Jakob J. Verbeek, Bill Triggs