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» An Effective Learning Method for Max-Min Neural Networks
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SAB
2010
Springer
117views Optimization» more  SAB 2010»
13 years 7 months ago
Indirectly Encoding Neural Plasticity as a Pattern of Local Rules
Biological brains can adapt and learn from past experience. In neuroevolution, i.e. evolving artificial neural networks (ANNs), one way that agents controlled by ANNs can evolve t...
Sebastian Risi, Kenneth O. Stanley
JMLR
2010
143views more  JMLR 2010»
13 years 3 months ago
Incremental Sigmoid Belief Networks for Grammar Learning
We propose a class of Bayesian networks appropriate for structured prediction problems where the Bayesian network's model structure is a function of the predicted output stru...
James Henderson, Ivan Titov
NN
2006
Springer
13 years 8 months ago
Propagation and control of stochastic signals through universal learning networks
The way of propagating and control of stochastic signals through Universal Learning Networks (ULNs) and its applications are proposed. ULNs have been already developed to form a s...
Kotaro Hirasawa, Shingo Mabu, Jinglu Hu
ICNC
2009
Springer
14 years 1 months ago
Knowledge Acquisition Approach Based on Rough Set and Artificial Neural Network in Product Design Process
In this paper, product structure is taken as knowledge acquisition point, and the effective knowledge acquisition path is discussed by establishing the associated relationship bet...
Changfeng Yuan, Wanlei Wang, Yan Chen
ICANN
2003
Springer
14 years 1 months ago
A Comparison of Model Aggregation Methods for Regression
Combining machine learning models is a means of improving overall accuracy.Various algorithms have been proposed to create aggregate models from other models, and two popular examp...
Zafer Barutçuoglu