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ICANN
2009
Springer
15 years 4 months ago
Constrained Learning Vector Quantization or Relaxed k-Separability
Neural networks and other sophisticated machine learning algorithms frequently miss simple solutions that can be discovered by a more constrained learning methods. Transition from ...
Marek Grochowski, Wlodzislaw Duch
IJCNN
2008
IEEE
15 years 6 months ago
Evolving a neural network using dyadic connections
—Since machine learning has become a tool to make more efficient design of sophisticated systems, we present in this paper a novel methodology to create powerful neural network ...
Andreas Huemer, Mario A. Góngora, David A. ...
ESANN
2006
15 years 1 months ago
Learning and discrimination through STDP in a top-down modulated associative memory
Abstract. This article underlines the learning and discrimination capabilities of a model of associative memory based on artificial networks of spiking neurons. Inspired from neuro...
Anthony Mouraud, Hélène Paugam-Moisy
AINA
2010
IEEE
14 years 8 months ago
Neural Network Trainer through Computer Networks
- This paper introduces a neural network training tool through computer networks. The following algorithms, such as neuron by neuron (NBN) [1][2], error back propagation (EBP), Lev...
Nam Pham, Hao Yu, Bogdan M. Wilamowski
FCCM
2007
IEEE
168views VLSI» more  FCCM 2007»
14 years 12 months ago
Discrete-Time Cellular Neural Networks in FPGA
This paper describes a novel architecture for the hardware implementation of non-linear multi-layer cellular neural networks. This makes it feasible to design CNNs with millions o...
J. Javier Martínez-Álvarez, F. Javie...