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ICML
2005
IEEE
14 years 8 months ago
Why skewing works: learning difficult Boolean functions with greedy tree learners
We analyze skewing, an approach that has been empirically observed to enable greedy decision tree learners to learn "difficult" Boolean functions, such as parity, in the...
Bernard Rosell, Lisa Hellerstein, Soumya Ray, Davi...
ACL
2009
13 years 5 months ago
Bayesian Learning of a Tree Substitution Grammar
Tree substitution grammars (TSGs) offer many advantages over context-free grammars (CFGs), but are hard to learn. Past approaches have resorted to heuristics. In this paper, we le...
Matt Post, Daniel Gildea
JMLR
2010
140views more  JMLR 2010»
13 years 2 months ago
Learning From Crowds
For many supervised learning tasks it may be infeasible (or very expensive) to obtain objective and reliable labels. Instead, we can collect subjective (possibly noisy) labels fro...
Vikas C. Raykar, Shipeng Yu, Linda H. Zhao, Gerard...
NAACL
2007
13 years 9 months ago
Tree Revision Learning for Dependency Parsing
We present a revision learning model for improving the accuracy of a dependency parser. The revision stage corrects the output of the base parser by means of revision rules learne...
Giuseppe Attardi, Massimiliano Ciaramita
ALT
2009
Springer
14 years 4 months ago
Learning Unknown Graphs
Motivated by a problem of targeted advertising in social networks, we introduce and study a new model of online learning on labeled graphs where the graph is initially unknown and...
Nicolò Cesa-Bianchi, Claudio Gentile, Fabio...