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» On learning with dissimilarity functions
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COLT
2010
Springer
13 years 7 months ago
Regret Minimization With Concept Drift
In standard online learning, the goal of the learner is to maintain an average loss that is "not too big" compared to the loss of the best-performing function in a fixed...
Koby Crammer, Yishay Mansour, Eyal Even-Dar, Jenni...
ICALT
2005
IEEE
14 years 3 months ago
Integrating Wireless Technology in Pocket Electronic Dictionary to Enhance Language Learning
We believe that with regard to the information technology applications in education, one student one computing device will be the future and long-term trend. Many related studies ...
Jen-Kai Liang, Tzu-Chien Liu, Hsue-Yie Wang, Tak-W...
ICML
2010
IEEE
13 years 10 months ago
Finite-Sample Analysis of LSTD
In this paper we consider the problem of policy evaluation in reinforcement learning, i.e., learning the value function of a fixed policy, using the least-squares temporal-differe...
Alessandro Lazaric, Mohammad Ghavamzadeh, Ré...
ESANN
2001
13 years 11 months ago
Transfer functions: hidden possibilities for better neural networks
Abstract. Sigmoidal or radial transfer functions do not guarantee the best generalization nor fast learning of neural networks. Families of parameterized transfer functions provide...
Wlodzislaw Duch, Norbert Jankowski
RSKT
2009
Springer
14 years 4 months ago
Learning Optimal Parameters in Decision-Theoretic Rough Sets
A game-theoretic approach for learning optimal parameter values for probabilistic rough set regions is presented. The parameters can be used to define approximation regions in a p...
Joseph P. Herbert, Jingtao Yao