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» On Online Learning of Decision Lists
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COGSCI
2006
75views more  COGSCI 2006»
14 years 11 months ago
A Hierarchical Bayesian Model of Human Decision-Making on an Optimal Stopping Problem
We consider human performance on an optimal stopping problem where people are presented with a list of numbers independently chosen from a uniform distribution. People are told ho...
Michael D. Lee
WWW
2005
ACM
16 years 10 days ago
Improving recommendation lists through topic diversification
In this work we present topic diversification, a novel method designed to balance and diversify personalized recommendation lists in order to reflect the user's complete spec...
Cai-Nicolas Ziegler, Sean M. McNee, Joseph A. Kons...
ICML
2005
IEEE
16 years 15 days ago
Bayesian sparse sampling for on-line reward optimization
We present an efficient "sparse sampling" technique for approximating Bayes optimal decision making in reinforcement learning, addressing the well known exploration vers...
Tao Wang, Daniel J. Lizotte, Michael H. Bowling, D...
ICIP
2009
IEEE
14 years 9 months ago
An incremental extremely random forest classifier for online learning and tracking
Decision trees have been widely used for online learning classification. Many approaches usually need large data stream to finish decision trees induction, as show notable limitat...
Aiping Wang, Guowei Wan, Zhiquan Cheng, Sikun Li
ICANN
2009
Springer
15 years 4 months ago
Learning from Examples to Generalize over Pose and Illumination
We present a neural system that recognizes faces under strong variations in pose and illumination. The generalization is learnt completely on the basis of examples of a subset of p...
Marco K. Müller, Rolf P. Würtz