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» Optimal feature selection for support vector machines
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ESANN
2008
13 years 9 months ago
On related violating pairs for working set selection in SMO algorithms
Sequential Minimal Optimization (SMO) is currently the most popular algorithm to solve large quadratic programs for Support Vector Machine (SVM) training. For many variants of this...
Tobias Glasmachers
PAMI
2010
185views more  PAMI 2010»
13 years 6 months ago
Evaluating Stability and Comparing Output of Feature Selectors that Optimize Feature Subset Cardinality
—Stability (robustness) of feature selection methods is a topic of recent interest, yet often neglected importance, with direct impact on the reliability of machine learning syst...
Petr Somol, Jana Novovicová
JMLR
2002
89views more  JMLR 2002»
13 years 7 months ago
The Set Covering Machine
We extend the classical algorithms of Valiant and Haussler for learning compact conjunctions and disjunctions of Boolean attributes to allow features that are constructed from the...
Mario Marchand, John Shawe-Taylor
ECCV
2008
Springer
14 years 9 months ago
Hierarchical Support Vector Random Fields: Joint Training to Combine Local and Global Features
Abstract. Recently, impressive results have been reported for the detection of objects in challenging real-world scenes. Interestingly however, the underlying models vary greatly e...
Paul Schnitzspan, Mario Fritz, Bernt Schiele
DAC
2008
ACM
14 years 8 months ago
Functional test selection based on unsupervised support vector analysis
Extensive software-based simulation continues to be the mainstream methodology for functional verification of designs. To optimize the use of limited simulation resources, coverag...
Onur Guzey, Li-C. Wang, Jeremy R. Levitt, Harry Fo...