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ICONIP
2008
15 years 3 months ago
Neural Network Regression for LHF Process Optimization
We present a system for regression using MLP neural networks with hyperbolic tangent functions in the input, hidden and output layer. The activation functions in the input and outp...
Miroslaw Kordos
NECO
2007
127views more  NECO 2007»
15 years 1 months ago
Visual Recognition and Inference Using Dynamic Overcomplete Sparse Learning
We present a hierarchical architecture and learning algorithm for visual recognition and other visual inference tasks such as imagination, reconstruction of occluded images, and e...
Joseph F. Murray, Kenneth Kreutz-Delgado
NIPS
1998
15 years 3 months ago
Global Optimisation of Neural Network Models via Sequential Sampling
We propose a novel strategy for training neural networks using sequential Monte Carlo algorithms. This global optimisation strategy allows us to learn the probability distribution...
João F. G. de Freitas, Mahesan Niranjan, Ar...
ICSNC
2007
IEEE
15 years 8 months ago
Movement Prediction Using Bayesian Learning for Neural Networks
Nowadays, path prediction is being extensively examined for use in the context of mobile and wireless computing towards more efficient network resource management schemes. Path pr...
Sherif Akoush, Ahmed Sameh
GECCO
2005
Springer
153views Optimization» more  GECCO 2005»
15 years 7 months ago
Evolving neural network ensembles for control problems
In neuroevolution, a genetic algorithm is used to evolve a neural network to perform a particular task. The standard approach is to evolve a population over a number of generation...
David Pardoe, Michael S. Ryoo, Risto Miikkulainen