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AMC
2008
99views more  AMC 2008»
15 years 6 months ago
Markov chain network training and conservation law approximations: Linking microscopic and macroscopic models for evolution
In this paper, a general framework for the analysis of a connection between the training of artificial neural networks via the dynamics of Markov chains and the approximation of c...
Roderick V. N. Melnik
NN
2006
Springer
15 years 6 months ago
Missing data imputation through GTM as a mixture of t-distributions
The Generative Topographic Mapping (GTM) was originally conceived as a probabilistic alternative to the well-known, neural networkinspired, Self-Organizing Maps. The GTM can also ...
Alfredo Vellido
ENGL
2007
89views more  ENGL 2007»
15 years 6 months ago
Similarity-based Heterogeneous Neural Networks
This research introduces a general class of functions serving as generalized neuron models to be used in artificial neural networks. They are cast in the common framework of comp...
Lluís A. Belanche Muñoz, Julio Jose ...
IWANN
1999
Springer
15 years 10 months ago
The Capacity and Attractor Basins of Associative Memory Models
The performance characteristics of five variants of the Hopfield network are examined. Two performance metrics are used: memory capacity, and a measure of the size of basins of att...
Neil Davey, S. P. Hunt
CIMCA
2005
IEEE
15 years 8 months ago
Accurate Electricity Load Forecasting with Artificial Neural Networks
In this paper we present a simple yet accurate model to forecast electricity load with Artificial Neural Networks (ANNs). We analyze the problem domain and choose the most adequat...
Daniel Ortiz Arroyo, Morten K. Skov, Quang Huynh