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» Algorithm Selection using Reinforcement Learning
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INFORMATICALT
2006
88views more  INFORMATICALT 2006»
15 years 2 months ago
Improving the Performances of Asynchronous Algorithms by Combining the Nogood Processors with the Nogood Learning Techniques
Abstract. The asynchronous techniques that exist within the programming with distributed constraints are characterized by the occurrence of the nogood values during the search for ...
Ionel Muscalagiu, Vladimir Cretu
124
Voted
NIPS
1998
15 years 4 months ago
Finite-Sample Convergence Rates for Q-Learning and Indirect Algorithms
In this paper, we address two issues of long-standing interest in the reinforcement learning literature. First, what kinds of performance guarantees can be made for Q-learning aft...
Michael J. Kearns, Satinder P. Singh
128
Voted
ECIR
2003
Springer
15 years 4 months ago
Representative Sampling for Text Classification Using Support Vector Machines
In order to reduce human efforts, there has been increasing interest in applying active learning for training text classifiers. This paper describes a straightforward active learni...
Zhao Xu, Kai Yu, Volker Tresp, Xiaowei Xu, Jizhi W...
112
Voted
ALT
2008
Springer
15 years 11 months ago
Active Learning in Multi-armed Bandits
In this paper we consider the problem of actively learning the mean values of distributions associated with a finite number of options (arms). The algorithms can select which opti...
András Antos, Varun Grover, Csaba Szepesv&a...
118
Voted
AAAI
1994
15 years 4 months ago
Improving Learning Performance Through Rational Resource Allocation
This article shows how rational analysis can be used to minimize learning cost for a general class of statistical learning problems. We discuss the factors that influence learning...
Jonathan Gratch, Steve A. Chien, Gerald DeJong