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IJCNN
2006
IEEE
15 years 9 months ago
Learning to Segment Any Random Vector
— We propose a method that takes observations of a random vector as input, and learns to segment each observation into two disjoint parts. We show how to use the internal coheren...
Aapo Hyvärinen, Jukka Perkiö
EPIA
2007
Springer
15 years 9 months ago
Generalization and Transfer Learning in Noise-Affected Robot Navigation Tasks
Abstract. When a robot learns to solve a goal-directed navigation task with reinforcement learning, the acquired strategy can usually exclusively be applied to the task that has be...
Lutz Frommberger
AOSE
2005
Springer
15 years 8 months ago
Aspects in Agent-Oriented Software Engineering: Lessons Learned
Several concerns in the development of multi-agent systems (MASs) cannot be represented in a modular fashion. In general, they inherently affect several system modules and cannot b...
Alessandro F. Garcia, Uirá Kulesza, Cl&aacu...
ESANN
2003
15 years 4 months ago
On the weight dynamics of recurrent learning
We derive continuous-time batch and online versions of the recently introduced efficient O(N2 ) training algorithm of Atiya and Parlos [2000] for fully recurrent networks. A mathem...
Ulf D. Schiller, Jochen J. Steil
109
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GECCO
2006
Springer
167views Optimization» more  GECCO 2006»
15 years 6 months ago
Genomic computing networks learn complex POMDPs
A genomic computing network is a variant of a neural network for which a genome encodes all aspects, both structural and functional, of the network. The genome is evolved by a gen...
David J. Montana, Eric Van Wyk, Marshall Brinn, Jo...