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» Selection of Subsets of Ordered Features in Machine Learning
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GECCO
2009
Springer
124views Optimization» more  GECCO 2009»
15 years 10 months ago
Reinforcement learning for games: failures and successes
We apply CMA-ES, an evolution strategy with covariance matrix adaptation, and TDL (Temporal Difference Learning) to reinforcement learning tasks. In both cases these algorithms se...
Wolfgang Konen, Thomas Bartz-Beielstein
162
Voted
FGCS
2000
104views more  FGCS 2000»
15 years 5 months ago
Protecting secret keys with personal entropy
Conventional encryption technology often requires users to protect a secret key by selecting a password or passphrase. While a good passphrase will only be known to the user, it a...
Carl M. Ellison, Chris Hall, Randy Milbert, Bruce ...
IJISTA
2007
124views more  IJISTA 2007»
15 years 6 months ago
Incremental learning for spoken affect classification and its application in call-centres
: This paper introduces a system for real-time incremental learning in a call-centre environment. The classifier used is a Support Vector Machine (SVM) and it is applied to telepho...
Donn Morrison, Ruili Wang, W. L. Xu, Liyanage C. D...
CIKM
2009
Springer
15 years 10 months ago
Efficient feature weighting methods for ranking
Feature weighting or selection is a crucial process to identify an important subset of features from a data set. Removing irrelevant or redundant features can improve the generali...
Hwanjo Yu, Jinoh Oh, Wook-Shin Han
SEMWEB
2005
Springer
15 years 11 months ago
Bootstrapping Ontology Alignment Methods with APFEL
Abstract. Ontology alignment is a prerequisite in order to allow for interoperation between different ontologies and many alignment strategies have been proposed to facilitate the ...
Marc Ehrig, Steffen Staab, York Sure