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» A Boosting Algorithm for Regression
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GECCO
2008
Springer
143views Optimization» more  GECCO 2008»
15 years 4 months ago
Enhanced generalized ant programming (EGAP)
This paper begins by reviewing different methods of automatic programming while emphasizing the technique of Ant Programming (AP). AP uses an ant foraging metaphor in which ants g...
Amirali Salehi-Abari, Tony White
AI
2000
Springer
15 years 2 months ago
Stochastic dynamic programming with factored representations
Markov decisionprocesses(MDPs) haveproven to be popular models for decision-theoretic planning, but standard dynamic programming algorithms for solving MDPs rely on explicit, stat...
Craig Boutilier, Richard Dearden, Moisés Go...
TCS
2010
15 years 1 months ago
Active learning in heteroscedastic noise
We consider the problem of actively learning the mean values of distributions associated with a finite number of options. The decision maker can select which option to generate t...
András Antos, Varun Grover, Csaba Szepesv&a...
JMLR
2010
104views more  JMLR 2010»
14 years 10 months ago
Increasing Feature Selection Accuracy for L1 Regularized Linear Models
L1 (also referred to as the 1-norm or Lasso) penalty based formulations have been shown to be effective in problem domains when noisy features are present. However, the L1 penalty...
Abhishek Jaiantilal, Gregory Z. Grudic
ML
2010
ACM
185views Machine Learning» more  ML 2010»
14 years 10 months ago
Learning to rank on graphs
Graph representations of data are increasingly common. Such representations arise in a variety of applications, including computational biology, social network analysis, web applic...
Shivani Agarwal