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TON
2010
126views more  TON 2010»
14 years 10 months ago
MAC Scheduling With Low Overheads by Learning Neighborhood Contention Patterns
Aggregate traffic loads and topology in multi-hop wireless networks may vary slowly, permitting MAC protocols to `learn' how to spatially coordinate and adapt contention patte...
Yung Yi, Gustavo de Veciana, Sanjay Shakkottai
MMM
2011
Springer
251views Multimedia» more  MMM 2011»
14 years 7 months ago
Randomly Projected KD-Trees with Distance Metric Learning for Image Retrieval
Abstract. Efficient nearest neighbor (NN) search techniques for highdimensional data are crucial to content-based image retrieval (CBIR). Traditional data structures (e.g., kd-tree...
Pengcheng Wu, Steven C. H. Hoi, Duc Dung Nguyen, Y...
ICML
2007
IEEE
16 years 4 months ago
Sample compression bounds for decision trees
We propose a formulation of the Decision Tree learning algorithm in the Compression settings and derive tight generalization error bounds. In particular, we propose Sample Compres...
Mohak Shah
140
Voted
NIPS
2000
15 years 5 months ago
From Margin to Sparsity
We present an improvement of Noviko 's perceptron convergence theorem. Reinterpreting this mistakebound as a margindependent sparsity guarantee allows us to give a PAC{style ...
Thore Graepel, Ralf Herbrich, Robert C. Williamson
MI
1993
15 years 5 months ago
Controlling a steel mill with BOXES
We describe an application of the BOXES learning algorithm of Michie and Chambers (1968) to a large-scale, real-world problem, namely, learning to control a steel mill. By applyin...
Michael McGarity, Claude Sammut, David P. Clements