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» On Generalization by Neural Networks
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GECCO
2005
Springer
196views Optimization» more  GECCO 2005»
16 years 1 days ago
Breeding swarms: a new approach to recurrent neural network training
This paper shows that a novel hybrid algorithm, Breeding Swarms, performs equal to, or better than, Genetic Algorithms and Particle Swarm Optimizers when training recurrent neural...
Matthew Settles, Paul Nathan, Terence Soule
ICANN
2001
Springer
15 years 11 months ago
On-Line Error Detection of Annotated Corpus Using Modular Neural Networks
This paper proposes an on-line error detecting method for a manually annotated corpus using min-max modular (M3 ) neural networks. The basic idea of the method is to use guaranteed...
Qing Ma, Bao-Liang Lu, Masaki Murata, Michinori Ic...
ESANN
2008
15 years 8 months ago
Initialization mechanism in Kohonen neural network implemented in CMOS technology
An initialization mechanism is presented for Kohonen neural network implemented in CMOS technology. Proper selection of initial values of neurons' weights has a large influenc...
Tomasz Talaska, Rafal Dlugosz
IJCAI
1997
15 years 8 months ago
An Effective Learning Method for Max-Min Neural Networks
Max and min operations have interesting properties that facilitate the exchange of information between the symbolic and real-valued domains. As such, neural networks that employ m...
Loo-Nin Teow, Kia-Fock Loe
IDEAL
2010
Springer
15 years 5 months ago
Dimension Reduction for Regression with Bottleneck Neural Networks
Dimension reduction for regression (DRR) deals with the problem of finding for high-dimensional data such low-dimensional representations, which preserve the ability to predict a ...
Elina Parviainen