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» Stochastic Finite Learning
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AAAI
1998
13 years 10 months ago
Tree Based Discretization for Continuous State Space Reinforcement Learning
Reinforcement learning is an effective technique for learning action policies in discrete stochastic environments, but its efficiency can decay exponentially with the size of the ...
William T. B. Uther, Manuela M. Veloso
ISCI
2007
170views more  ISCI 2007»
13 years 8 months ago
Automatic learning of cost functions for graph edit distance
Graph matching and graph edit distance have become important tools in structural pattern recognition. The graph edit distance concept allows us to measure the structural similarit...
Michel Neuhaus, Horst Bunke
BC
2002
84views more  BC 2002»
13 years 8 months ago
Hebbian spike-driven synaptic plasticity for learning patterns of mean firing rates
Synaptic plasticity is believed to underlie the formation of appropriate patterns of connectivity that stabilize stimulus-selective reverberations in the cortex. Here we present a ...
Stefano Fusi
IJON
2010
119views more  IJON 2010»
13 years 7 months ago
Hyperparameter learning in probabilistic prototype-based models
We present two approaches to extend Robust Soft Learning Vector Quantization (RSLVQ). This algorithm for nearest prototype classification is derived from an explicit cost functio...
Petra Schneider, Michael Biehl, Barbara Hammer
ACL
2011
13 years 18 days ago
Learning to Win by Reading Manuals in a Monte-Carlo Framework
This paper presents a novel approach for leveraging automatically extracted textual knowledge to improve the performance of control applications such as games. Our ultimate goal i...
S. R. K. Branavan, David Silver, Regina Barzilay