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NPL
2000
146views more  NPL 2000»
15 years 6 months ago
Competitive and Temporal Inhibition Structures with Spiking Neurons
The paper describes the implementation of competitive neural structures based on a spiking neural model that includes multiplicative or shunting synapses enabling non-saturated sta...
Eduardo Ros Vidal, Francisco J. Pelayo, P. Martin-...
IJON
2002
100views more  IJON 2002»
15 years 6 months ago
A computational neuroscience account of visual neglect
On the basis of a computational and neurodynamical model, we investigate a cognitive impairment in stroke patients termed visual neglect. The model is based on the "biased co...
Dietmar Heinke, Gustavo Deco, Josef Zihl, Glyn W. ...
ML
2000
ACM
124views Machine Learning» more  ML 2000»
15 years 6 months ago
Text Classification from Labeled and Unlabeled Documents using EM
This paper shows that the accuracy of learned text classifiers can be improved by augmenting a small number of labeled training documents with a large pool of unlabeled documents. ...
Kamal Nigam, Andrew McCallum, Sebastian Thrun, Tom...
IWANN
1999
Springer
15 years 11 months ago
Using Temporal Neighborhoods to Adapt Function Approximators in Reinforcement Learning
To avoid the curse of dimensionality, function approximators are used in reinforcement learning to learn value functions for individual states. In order to make better use of comp...
R. Matthew Kretchmar, Charles W. Anderson

Book
778views
17 years 4 months ago
Gaussian Processes for Machine Learning
"Gaussian processes (GPs) provide a principled, practical, probabilistic approach to learning in kernel machines. GPs have received increased attention in the machine-learning...
Carl Edward Rasmussen and Christopher K. I. Willia...