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» Boosting and Hard-Core Sets
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97
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CORR
2000
Springer
132views Education» more  CORR 2000»
15 years 8 days ago
A Comparison between Supervised Learning Algorithms for Word Sense Disambiguation
This paper describes a set of comparative experiments, including cross{corpus evaluation, between ve alternative algorithms for supervised Word Sense Disambiguation (WSD), namely ...
Gerard Escudero, Lluís Màrquez, Germ...
124
Voted
TSP
2010
14 years 7 months ago
Learning graphical models for hypothesis testing and classification
Sparse graphical models have proven to be a flexible class of multivariate probability models for approximating high-dimensional distributions. In this paper, we propose techniques...
Vincent Y. F. Tan, Sujay Sanghavi, John W. Fisher ...
IWCLS
2007
Springer
15 years 6 months ago
Evolving Fuzzy Rules with UCS: Preliminary Results
This paper presents Fuzzy-UCS, a Michigan-style Learning Fuzzy-Classifier System designed for supervised learning tasks. FuzzyUCS combines the generalization capabilities of UCS w...
Albert Orriols-Puig, Jorge Casillas, Ester Bernad&...
ML
2000
ACM
15 years 7 days ago
Randomizing Outputs to Increase Prediction Accuracy
Bagging and boosting reduce error by changing both the inputs and outputs to form perturbed training sets, grow predictors on these perturbed training sets and combine them. A que...
Leo Breiman
AI
2011
Springer
14 years 7 months ago
State agnostic planning graphs: deterministic, non-deterministic, and probabilistic planning
Planning graphs have been shown to be a rich source of heuristic information for many kinds of planners. In many cases, planners must compute a planning graph for each element of ...
Daniel Bryce, William Cushing, Subbarao Kambhampat...