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» Learning Mappings with Neural Network
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ESANN
2001
15 years 7 months ago
Transfer functions: hidden possibilities for better neural networks
Abstract. Sigmoidal or radial transfer functions do not guarantee the best generalization nor fast learning of neural networks. Families of parameterized transfer functions provide...
Wlodzislaw Duch, Norbert Jankowski
137
Voted
CSB
2005
IEEE
166views Bioinformatics» more  CSB 2005»
15 years 11 months ago
Artificial Neural Networks to Predict Daylily Hybrids
Artificial Neural Networks (ANN) were employed to predict daylily (Hemerocalli spp.) hybrids from known characteristics of parents used in hybridization. Features such as height, ...
Ramana M. Gosukonda, Masoud Naghedolfeizi, Johnny ...
GECCO
2010
Springer
181views Optimization» more  GECCO 2010»
15 years 11 months ago
Evolving neural networks in compressed weight space
We propose a new indirect encoding scheme for neural networks in which the weight matrices are represented in the frequency domain by sets of Fourier coefficients. This scheme exp...
Jan Koutnik, Faustino J. Gomez, Jürgen Schmid...
ECAL
2005
Springer
15 years 11 months ago
Measuring Diversity in Populations Employing Cultural Learning in Dynamic Environments
Abstract. This paper examines the effect of cultural learning on a population of neural networks. We compare the genotypic and phenotypic diversity of populations employing only p...
Dara Curran, Colm O'Riordan
ICANN
2003
Springer
15 years 11 months ago
Optimal Hebbian Learning: A Probabilistic Point of View
Many activity dependent learning rules have been proposed in order to model long-term potentiation (LTP). Our aim is to derive a spike time dependent learning rule from a probabili...
Jean-Pascal Pfister, David Barber, Wulfram Gerstne...