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» Optimizing number of hidden neurons in neural networks
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NECO
2007
115views more  NECO 2007»
13 years 7 months ago
Training Recurrent Networks by Evolino
In recent years, gradient-based LSTM recurrent neural networks (RNNs) solved many previously RNN-unlearnable tasks. Sometimes, however, gradient information is of little use for t...
Jürgen Schmidhuber, Daan Wierstra, Matteo Gag...
ICANN
2001
Springer
14 years 11 days ago
A Computational Intelligence Approach to Optimization with Unknown Objective Functions
In many practical engineering design problems, the form of objective function is not given explicitly in terms of design variables. Given the value of design variables, under this ...
Hirotaka Nakayama, Masao Arakawa, Rie Sasaki
GECCO
2008
Springer
179views Optimization» more  GECCO 2008»
13 years 9 months ago
A hybrid method for tuning neural network for time series forecasting
This paper presents an study about a new Hybrid method GRASPES - for time series prediction, inspired in F. Takens theorem and based on a multi-start metaheuristic for combinatori...
Aranildo Rodrigues Lima Junior, Tiago Alessandro E...
ECCC
2000
158views more  ECCC 2000»
13 years 7 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
NPL
2002
110views more  NPL 2002»
13 years 7 months ago
Biologically Plausible Associative Memory: Continuous Unit Response + Stochastic Dynamics
A neural network model of associative memory is presented which unifies the two historically more relevant enhancements to the basic Little-Hopfield discrete model: the graded resp...
Enrique Carlos Segura Meccia, Roberto P. J. Perazz...