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ICONIP
2009
15 years 4 months ago
Learning Gaussian Process Models from Uncertain Data
It is generally assumed in the traditional formulation of supervised learning that only the outputs data are uncertain. However, this assumption might be too strong for some learni...
Patrick Dallaire, Camille Besse, Brahim Chaib-draa
ICANN
2010
Springer
15 years 7 months ago
Model of the Hippocampal Learning of Spatio-temporal Sequences
We propose a model of the hippocampus aimed at learning the timed association between subsequent sensory events. The properties of the neural network allow it to learn and predict ...
Julien Hirel, Philippe Gaussier, Mathias Quoy
AROBOTS
1999
104views more  AROBOTS 1999»
15 years 6 months ago
Reinforcement Learning Soccer Teams with Incomplete World Models
We use reinforcement learning (RL) to compute strategies for multiagent soccer teams. RL may pro t signi cantly from world models (WMs) estimating state transition probabilities an...
Marco Wiering, Rafal Salustowicz, Jürgen Schm...
ICDM
2008
IEEE
112views Data Mining» more  ICDM 2008»
16 years 1 months ago
Supervised Inductive Learning with Lotka-Volterra Derived Models
We present a classification algorithm built on our adaptation of the Generalized Lotka-Volterra model, well-known in mathematical ecology. The training algorithm itself consists ...
Karen Hovsepian, Peter Anselmo, Subhasish Mazumdar
JMLR
2011
187views more  JMLR 2011»
15 years 1 months ago
Exploitation of Machine Learning Techniques in Modelling Phrase Movements for Machine Translation
We propose a distance phrase reordering model (DPR) for statistical machine translation (SMT), where the aim is to learn the grammatical rules and context dependent changes using ...
Yizhao Ni, Craig Saunders, Sándor Szedm&aac...