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» Learning with the Set Covering Machine
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ECML
2004
Springer
14 years 4 months ago
Improving Random Forests
Random forests are one of the most successful ensemble methods which exhibits performance on the level of boosting and support vector machines. The method is fast, robust to noise,...
Marko Robnik-Sikonja
EUROGP
2003
Springer
14 years 4 months ago
Evolving Finite State Transducers: Some Initial Explorations
Finite state transducers (FSTs) are finite state machines that map strings in a source domain into strings in a target domain. While there are many reports in the literature of ev...
Simon M. Lucas
FLAIRS
2007
14 years 1 months ago
Guiding Inference with Policy Search Reinforcement Learning
Symbolic reasoning is a well understood and effective approach to handling reasoning over formally represented knowledge; however, simple symbolic inference systems necessarily sl...
Matthew E. Taylor, Cynthia Matuszek, Pace Reagan S...
ENTCS
2010
99views more  ENTCS 2010»
13 years 10 months ago
State Based Robustness Testing for Components
Component based development allows to build software upon existing components and promises to improve software reuse and reduce costs. To gain reliability of a component based sys...
Bin Lei, Zhiming Liu, Charles Morisset, Xuandong L...
ECML
2003
Springer
14 years 4 months ago
Learning Context Free Grammars in the Limit Aided by the Sample Distribution
We present an algorithm for learning context free grammars from positive structural examples (unlabeled parse trees). The algorithm receives a parameter in the form of a finite se...
Yoav Seginer