A Hierarchical Neural Network Document Classifier with Linguistic Feature Selection
Applied Intelligence
A Modified Backpropagation Training Algorithm for Feedforward Neural Networks
Neural Processing Letters
A Hybrid Training Algorithm for Feedforward Neural Networks
Neural Processing Letters
ANN-based estimator for distillation using Levenberg-Marquardt approach
Engineering Applications of Artificial Intelligence
Time-frequency feature extraction of newborn EEG seizure using SVD-based techniques
EURASIP Journal on Applied Signal Processing
A Local-Information-Based Blind Image Restoration Algorithm Using a MLP
ICANNGA '07 Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part II
Identification of finite state automata with a class of recurrent neural networks
IEEE Transactions on Neural Networks
Diagnosis of hypoglycemic episodes using a neural network based rule discovery system
Expert Systems with Applications: An International Journal
Application of feedforward neural network in the study of dissociated gas flow along the porous wall
Expert Systems with Applications: An International Journal
An improved three-term optical backpropagation algorithm
International Journal of Artificial Intelligence and Soft Computing
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Although the Levenberg-Marquardt (LM) algorithm has been extensively applied as a neural-network training method, it suffers from being very expensive, both in memory and number of operations required, when the network to be trained has a significant number of adaptive weights. In this paper, the behavior of a recently proposed variation of this algorithm is studied. This new method is based on the application of the concept of neural neighborhoods to the LM algorithm. It is shown that, by performing an LM step on a single neighborhood at each training iteration, not only significant savings in memory occupation and computing effort are obtained, but also, the overall performance of the LM method can be increased.