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ICML
2006
IEEE
14 years 9 months ago
Active learning via transductive experimental design
This paper considers the problem of selecting the most informative experiments x to get measurements y for learning a regression model y = f(x). We propose a novel and simple conc...
Kai Yu, Jinbo Bi, Volker Tresp
ICML
2002
IEEE
14 years 9 months ago
Coordinated Reinforcement Learning
We present several new algorithms for multiagent reinforcement learning. A common feature of these algorithms is a parameterized, structured representation of a policy or value fu...
Carlos Guestrin, Michail G. Lagoudakis, Ronald Par...
ML
2007
ACM
153views Machine Learning» more  ML 2007»
13 years 7 months ago
Multi-Class Learning by Smoothed Boosting
AdaBoost.OC has been shown to be an effective method in boosting “weak” binary classifiers for multi-class learning. It employs the Error-Correcting Output Code (ECOC) method ...
Rong Jin, Jian Zhang 0003
ICMLA
2009
13 years 6 months ago
Transformation Learning Via Kernel Alignment
This article proposes an algorithm to automatically learn useful transformations of data to improve accuracy in supervised classification tasks. These transformations take the for...
Andrew Howard, Tony Jebara
CSL
2008
Springer
13 years 8 months ago
A stopping criterion for active learning
Active learning (AL) is a framework that attempts to reduce the cost of annotating training material for statistical learning methods. While a lot of papers have been presented on...
Andreas Vlachos