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SAT
2009
Springer
111views Hardware» more  SAT 2009»
13 years 12 months ago
Restart Strategy Selection Using Machine Learning Techniques
Abstract. Restart strategies are an important factor in the performance of conflict-driven Davis Putnam style SAT solvers. Selecting a good restart strategy for a problem instance...
Shai Haim, Toby Walsh
ICASSP
2008
IEEE
13 years 11 months ago
Discriminative feature selection for hidden Markov models using Segmental Boosting
We address the feature selection problem for hidden Markov models (HMMs) in sequence classification. Temporal correlation in sequences often causes difficulty in applying featur...
Pei Yin, Irfan A. Essa, Thad Starner, James M. Reh...
ILP
2003
Springer
13 years 10 months ago
A Comparative Evaluation of Feature Set Evolution Strategies for Multirelational Boosting
Boosting has established itself as a successful technique for decreasing the generalization error of classification learners by basing predictions on ensembles of hypotheses. Whil...
Susanne Hoche, Stefan Wrobel
PRIS
2008
13 years 6 months ago
Comparison of Adaboost and ADTboost for Feature Subset Selection
Abstract. This paper addresses the problem of feature selection within classification processes. We present a comparison of a feature subset selection with respect to two boosting ...
Martin Drauschke, Wolfgang Förstner
ISI
2005
Springer
13 years 10 months ago
Selective Fusion for Speaker Verification in Surveillance
This paper presents an improved speaker verification technique that is especially appropriate for surveillance scenarios. The main idea is a metalearning scheme aimed at improving ...
Yosef A. Solewicz, Moshe Koppel