A new growing pruning deep learning neural network algorithm (GP-DLNN)

During the last decade, a significant research progress has been drawn in both the theoretical aspects and the applications of Deep Learning Neural Networks. Besides their spectacular applications, optimal architectures of these neural networks may speed up the learning process and exhibit better ge...

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Veröffentlicht in:Neural computing & applications Jg. 32; H. 24; S. 18143 - 18159
Hauptverfasser: Zemouri, Ryad, Omri, Nabil, Fnaiech, Farhat, Zerhouni, Noureddine, Fnaiech, Nader
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London Springer London 01.12.2020
Springer Nature B.V
Springer Verlag
Schlagworte:
Algorithms
Artificial Intelligence
Artificial neural networks
Computational Biology/Bioinformatics
Computational Science and Engineering
Computer architecture
Computer Science
Data Mining and Knowledge Discovery
Datasets
Deep learning
Image Processing and Computer Vision
Machine Learning
Neural networks
Optimization
Probability and Statistics in Computer Science
Wsom 2017
Deep learning
Constructive neural networks
Deep neural networks
Growing algorithm
Pruning algorithm
ISSN:0941-0643, 1433-3058
Online-Zugang:Volltext
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Zusammenfassung:During the last decade, a significant research progress has been drawn in both the theoretical aspects and the applications of Deep Learning Neural Networks. Besides their spectacular applications, optimal architectures of these neural networks may speed up the learning process and exhibit better generalization results. So far, many growing and pruning algorithms have been proposed by many researchers to deal with the optimization of standard Feedforward Neural Network architectures. However, applying both the growing and the pruning on the same net may lead a good model for a big data set and hence good selection results. This work is devoted to propose a new Growing and pruning Learning algorithm for Deep Neural Networks. This new algorithm is presented and applied on diverse medical data sets. It is shown that this algorithm outperforms various other artificial intelligent techniques in terms of accuracy and simplicity of the resulting architecture.
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ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-019-04196-8