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NIPS
2004
15 years 5 months ago
Brain Inspired Reinforcement Learning
Successful application of reinforcement learning algorithms often involves considerable hand-crafting of the necessary non-linear features to reduce the complexity of the value fu...
François Rivest, Yoshua Bengio, John Kalask...
ECAI
2004
Springer
15 years 9 months ago
Towards Efficient Learning of Neural Network Ensembles from Arbitrarily Large Datasets
Advances in data collection technologies allow accumulation of large and high dimensional datasets and provide opportunities for learning high quality classification and regression...
Kang Peng, Zoran Obradovic, Slobodan Vucetic
SODA
2010
ACM
190views Algorithms» more  SODA 2010»
16 years 1 months ago
One-Counter Markov Decision Processes
We study the computational complexity of some central analysis problems for One-Counter Markov Decision Processes (OC-MDPs), a class of finitely-presented, countable-state MDPs. O...
Tomas Brazdil, Vaclav Brozek, Kousha Etessami, Ant...
KES
2007
Springer
15 years 10 months ago
A Hybrid Symbolic-Statistical Approach to Modeling Metabolic Networks
Biological systems consist of many components and interactions between them. In Systems Biology the principal problem is modeling complex biological systems and reconstructing inte...
Marenglen Biba, Stefano Ferilli, Nicola Di Mauro, ...
IJNCR
2010
59views more  IJNCR 2010»
15 years 1 months ago
Cognitively Inspired Neural Network for Recognition of Situations
we present a cognitively inspired mathematical learning framework called Neural Modeling Fields (NMF). We apply it to learning and recognition of situations composed of objects. NM...
Roman Ilin, Leonid I. Perlovsky