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912,182
Global Optimization for Neural Network Training
- IEEE Computer
, 1996
"... In this paper, we study various supervised learning methods for training feed-forward neural networks. In general, such learning can be considered as a nonlinear global optimization problem in which the goal is to minimize a nonlinear error function that spans the space of weights using heuristic st ..."
Abstract
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Cited by 53 (12 self)
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In this paper, we study various supervised learning methods for training feed-forward neural networks. In general, such learning can be considered as a nonlinear global optimization problem in which the goal is to minimize a nonlinear error function that spans the space of weights using heuristic
Trajectory Methods for Neural Network Training
- In: Proceedings of the IASTED International Conference on Artificial Intelligence and Applications (AIA’04
, 2004
"... A new class of methods for training multilayer feedforward neural networks is proposed. The proposed class of methods draws from methods for solving initial value problems of ordinary differential equations, and belong to the subclass of trajectory methods. The training of a multilayer feedforward n ..."
Abstract
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Cited by 1 (1 self)
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A new class of methods for training multilayer feedforward neural networks is proposed. The proposed class of methods draws from methods for solving initial value problems of ordinary differential equations, and belong to the subclass of trajectory methods. The training of a multilayer feedforward
Behaviour in 0 of the Neural Networks Training Cost
, 1997
"... . We study the behaviour in zero of the derivatives of the cost function used when training non-linear neural networks. It is shown that a fair number of first, second and higher order derivatives vanish in zero, validating the belief that 0 is a peculiar and potentially harmful location. These calc ..."
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. We study the behaviour in zero of the derivatives of the cost function used when training non-linear neural networks. It is shown that a fair number of first, second and higher order derivatives vanish in zero, validating the belief that 0 is a peculiar and potentially harmful location
Online Neural Network Training for Automatic
- In Leszek Rutkowski, Jörg H. Siekmann, Ryszard Tadeusiewicz, and Lotfi
, 2003
"... Myocardial ischemia is caused by a lack of oxygen and nutrients to the contractile cells and may lead to myocardial infarction with its severe consequence of heart failure and arrhythmia. An electrocardiogram (ECG) represents a recording of changes occurring in the electrical potentials between ..."
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propose a new classification methodology that draws from the disciplines of clustering and artificial neural networks, and apply it to the problem of myocardial ischemia detection. The results obtained are promising.
Genetic Algorithms for Neural Network Training
, 1995
"... The use of both, genetic algorithms and artificial neural networks, was originally motivated by the astonishing success of these concepts in their biological counterparts. Despite their totally different approaches, both can merely be seen as optimisation methods which are used in a wide range of ap ..."
Abstract
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Cited by 1 (0 self)
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of applications, where traditional methods often prove to be unsatisfactory. This project deals about how genetic methods can be used as training strategies for neural networks and how they can be implemented on a distributed memory system. The main point of interest is, whether they provide a reasonable
algorithm for feedforward neural network training
"... A hybrid particle swarm optimization–back-propagation ..."
Some Computational Complexity Aspects of Neural Network Training
, 1996
"... . We adress the problem of obtaining an estimate of the computational complexity of a training algorithm, as a function of the number of patterns and parameters. On these grounds, we compare two different training algorithm, namely the leave-one-out cross-validation and the regularisation method. 1 ..."
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Cited by 1 (1 self)
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Neural Network training Neural networks have been extensively used as non-linear regression tools. We will here consider the case of a multi-layered perceptrons model, which provides a mapping from IR I to IR S . The mapping is parameterized by a vector w containing P parameters. The S
Neural network training with constrained integer weights
, 1999
"... Abstract- In this contribution we present neural network training algorithms, which are based on the differential evolution (DE) strategies introduced by Storn and Price [Journal of Global Optimization 11, 341-359, 19971. These strategies are applied to train neural networks with small integer weigh ..."
Abstract
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Cited by 17 (15 self)
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Abstract- In this contribution we present neural network training algorithms, which are based on the differential evolution (DE) strategies introduced by Storn and Price [Journal of Global Optimization 11, 341-359, 19971. These strategies are applied to train neural networks with small integer
Results 1 - 10
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912,182
