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CORR
2010
Springer
105views Education» more  CORR 2010»
15 years 2 months ago
Optimism in Reinforcement Learning Based on Kullback-Leibler Divergence
We consider model-based reinforcement learning in finite Markov Decision Processes (MDPs), focussing on so-called optimistic strategies. Optimism is usually implemented by carryin...
Sarah Filippi, Olivier Cappé, Aurelien Gari...
JMLR
2006
116views more  JMLR 2006»
15 years 3 months ago
Point-Based Value Iteration for Continuous POMDPs
We propose a novel approach to optimize Partially Observable Markov Decisions Processes (POMDPs) defined on continuous spaces. To date, most algorithms for model-based POMDPs are ...
Josep M. Porta, Nikos A. Vlassis, Matthijs T. J. S...
MICAI
2004
Springer
15 years 9 months ago
A Biologically Motivated and Computationally Efficient Natural Language Processor
Abstract. Conventional artificial neural network models lack many physiological properties of the neuron. Current learning algorithms are more concerned to computational performanc...
João Luís Garcia Rosa
119
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SODA
2010
ACM
160views Algorithms» more  SODA 2010»
16 years 1 months ago
Solving Simple Stochastic Tail Games
Stochastic games are a natural model for open reactive processes: one player represents the controller and his opponent represents a hostile environment. The evolution of the syste...
Hugo Gimbert, Florian Horn
ICMLA
2007
15 years 5 months ago
Scalable optimal linear representation for face and object recognition
Optimal Component Analysis (OCA) is a linear method for feature extraction and dimension reduction. It has been widely used in many applications such as face and object recognitio...
Yiming Wu, Xiuwen Liu, Washington Mio
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