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ICANN
2010
Springer
15 years 7 months ago
Empirical Analysis of the Divergence of Gibbs Sampling Based Learning Algorithms for Restricted Boltzmann Machines
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...
Asja Fischer, Christian Igel
ML
2006
ACM
110views Machine Learning» more  ML 2006»
15 years 6 months ago
Classification-based objective functions
Backpropagation, similar to most learning algorithms that can form complex decision surfaces, is prone to overfitting. This work presents classification-based objective functions, ...
Michael Rimer, Tony Martinez
NN
2006
Springer
163views Neural Networks» more  NN 2006»
15 years 6 months ago
Machine learning approaches for estimation of prediction interval for the model output
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...
Durga L. Shrestha, Dimitri P. Solomatine
EC
2000
241views ECommerce» more  EC 2000»
15 years 6 months ago
Cooperative Coevolution: An Architecture for Evolving Coadapted Subcomponents
To successfully apply evolutionary algorithms to the solution of increasingly complex problems, we must develop effective techniques for evolving solutions in the form of interact...
Mitchell A. Potter, Kenneth A. De Jong
ECCC
2000
158views more  ECCC 2000»
15 years 6 months ago
On the Computational Power of Winner-Take-All
This article initiates a rigorous theoretical analysis of the computational power of circuits that employ modules for computing winner-take-all. Computational models that involve ...
Wolfgang Maass