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NIPS
2003
15 years 8 months ago
Can We Learn to Beat the Best Stock
A novel algorithm for actively trading stocks is presented. While traditional universal algorithms (and technical trading heuristics) attempt to predict winners or trends, our app...
Allan Borodin, Ran El-Yaniv, Vincent Gogan
175
Voted
SERP
2003
15 years 8 months ago
Specification of the Verity Learning Companion and Self-Assessment Tool
In this paper, the specification of Verity, a webbased instructional tool, is presented. Verity is intended to be used as a learning assistant and self-assessment tool, more than ...
Sergiu Dascalu, Daniela Saru, Ryan Simpson, Justin...
NIPS
1994
15 years 8 months ago
Generalization in Reinforcement Learning: Safely Approximating the Value Function
To appear in: G. Tesauro, D. S. Touretzky and T. K. Leen, eds., Advances in Neural Information Processing Systems 7, MIT Press, Cambridge MA, 1995. A straightforward approach to t...
Justin A. Boyan, Andrew W. Moore
151
Voted
JACIII
2006
97views more  JACIII 2006»
15 years 6 months ago
Opposition-Based Reinforcement Learning
In this paper a method for image segmentation using an opposition-based reinforcement learning scheme is introduced. We use this agent-based approach to optimally find the appropri...
Hamid R. Tizhoosh
NECO
2002
104views more  NECO 2002»
15 years 6 months ago
An Unsupervised Ensemble Learning Method for Nonlinear Dynamic State-Space Models
A Bayesian ensemble learning method is introduced for unsupervised extraction of dynamic processes from noisy data. The data are assumed to be generated by an unknown nonlinear ma...
Harri Valpola, Juha Karhunen