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AAAI
2006
15 years 8 months ago
Boosting Expert Ensembles for Rapid Concept Recall
Many learning tasks in adversarial domains tend to be highly dependent on the opponent. Predefined strategies optimized for play against a specific opponent are not likely to succ...
Achim Rettinger, Martin Zinkevich, Michael H. Bowl...
ML
2008
ACM
134views Machine Learning» more  ML 2008»
15 years 6 months ago
Multilabel classification via calibrated label ranking
Label ranking studies the problem of learning a mapping from instances to rankings over a predefined set of labels. Hitherto existing approaches to label ranking implicitly operat...
Johannes Fürnkranz, Eyke Hüllermeier, En...
ICML
2001
IEEE
16 years 7 months ago
Learning to Generate Fast Signal Processing Implementations
A single signal processing algorithm can be represented by many mathematically equivalent formulas. However, when these formulas are implemented in code and run on real machines, ...
Bryan Singer, Manuela M. Veloso
STOC
2005
ACM
198views Algorithms» more  STOC 2005»
16 years 7 months ago
On lattices, learning with errors, random linear codes, and cryptography
Our main result is a reduction from worst-case lattice problems such as GAPSVP and SIVP to a certain learning problem. This learning problem is a natural extension of the `learnin...
Oded Regev
ICDM
2009
IEEE
199views Data Mining» more  ICDM 2009»
16 years 1 months ago
Active Learning with Adaptive Heterogeneous Ensembles
—One common approach to active learning is to iteratively train a single classifier by choosing data points based on its uncertainty, but it is nontrivial to design uncertainty ...
Zhenyu Lu, Xindong Wu, Josh Bongard