5381 search results - page 224 / 1077 » Artificial neural networks |
210
click to vote
ICANN
2010
Springer
15 years 7 months ago
2010
Springer
Abstract. Learning algorithms relying on Gibbs sampling based stochastic approximations of the log-likelihood gradient have become a common way to train Restricted Boltzmann Machin...
188
click to vote
NN
2006
Springer
15 years 6 months ago
2006
Springer
A novel method for estimating prediction uncertainty using machine learning techniques is presented. Uncertainty is expressed in the form of the two quantiles (constituting the pr...
180
click to vote
NN
2002
Springer
15 years 6 months ago
2002
Springer
We propose a new scheme for enlarging generalized learning vector quantization (GLVQ) with weighting factors for the input dimensions. The factors allow an appropriate scaling of ...
827
click to vote
Tutorial
Neural networks learn by adjusting numeric values called weights and thresholds. A weight specifies how strong of a connection exists between two neurons. A threshold is a value,...
172
click to vote
IJCNN
2006
IEEE
16 years 20 days ago
2006
IEEE
— The idea of using evolutionary techniques to optimize the performance of neural networks is now widely used, but some approaches have been found to result in the evolution of r...