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» Algorithm Selection using Reinforcement Learning
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JCP
2007
143views more  JCP 2007»
15 years 2 months ago
Noisy K Best-Paths for Approximate Dynamic Programming with Application to Portfolio Optimization
Abstract— We describe a general method to transform a non-Markovian sequential decision problem into a supervised learning problem using a K-bestpaths algorithm. We consider an a...
Nicolas Chapados, Yoshua Bengio
130
Voted
CEC
2005
IEEE
15 years 5 months ago
A note on the population based incremental learning with infinite population size
In this paper, we study the dynamical properties of the population based incremental learning (PBIL) algorithm when it uses truncation, proportional, and Boltzmann selection schema...
Reza Rastegar, Mohammad Reza Meybodi
ICML
1994
IEEE
15 years 6 months ago
Efficient Algorithms for Minimizing Cross Validation Error
Model selection is important in many areas of supervised learning. Given a dataset and a set of models for predicting with that dataset, we must choose the model which is expected...
Andrew W. Moore, Mary S. Lee
152
Voted
TMC
2008
78views more  TMC 2008»
15 years 2 months ago
SELECT: Self-Learning Collision Avoidance for Wireless Networks
The limited number of orthogonal channels and autonomous installations of hotspots and home wireless networks often leave neighboring 802.11 basic service sets (BSSs) operating on ...
Chun-cheng Chen, Eunsoo Seo, Hwangnam Kim, Haiyun ...
131
Voted
GECCO
2008
Springer
128views Optimization» more  GECCO 2008»
15 years 4 months ago
Adapted Pittsburgh classifier system: building accurate strategies in non markovian environments
This paper focuses on the study of the behavior of a genetic algorithm based classifier system, the Adapted Pittsburgh Classifier System (A.P.C.S), on maze type environments con...
Gilles Énée, Mathias Péroumal...