Algorithmic Synthesis in Neural Network Training for Pattern Recognition

2003 ◽  
pp. 703-739
Author(s):  
Konstantinos Sirlantzis
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Neural Network Training ◽  
Network Training

scholarly journals The training set in the problem of pattern recognition using neural networks

2015 ◽  
Vol 11 (2) ◽  
pp. 94-98
Author(s):  
Vitaly M Tatyankin ◽  
Irina S Dyubko
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Pattern Recognition ◽  
Training Sample ◽  
Neural Network Training ◽  
Training Set ◽  
Network Training ◽  
Practical Recommendations

The article discusses approaches to the formation of the training sample in the problem of recognition of monochrome images. It is shown that the variation of the training sample, allows to reduce the error of neural network training. Practical recommendations for the formation of a training sample.

scholarly journals A Geometric Perspective on Information Plane Analysis

Entropy ◽  
2021 ◽  
Vol 23 (6) ◽  
pp. 711
Author(s):  
Mina Basirat ◽  
Bernhard C. Geiger ◽  
Peter M. Roth
Keyword(s):  
Neural Network ◽  
Mutual Information ◽  
Geometric Interpretation ◽  
Neural Network Training ◽  
Neural Network Learning ◽  
Network Learning ◽  
Plane Analysis ◽  
Network Training ◽  
Hidden Layer ◽  
The Impact

Information plane analysis, describing the mutual information between the input and a hidden layer and between a hidden layer and the target over time, has recently been proposed to analyze the training of neural networks. Since the activations of a hidden layer are typically continuous-valued, this mutual information cannot be computed analytically and must thus be estimated, resulting in apparently inconsistent or even contradicting results in the literature. The goal of this paper is to demonstrate how information plane analysis can still be a valuable tool for analyzing neural network training. To this end, we complement the prevailing binning estimator for mutual information with a geometric interpretation. With this geometric interpretation in mind, we evaluate the impact of regularization and interpret phenomena such as underfitting and overfitting. In addition, we investigate neural network learning in the presence of noisy data and noisy labels.

scholarly journals An Adaptive Memory Multi-Batch L-BFGS Algorithm for Neural Network Training

2020 ◽  
Vol 53 (2) ◽  
pp. 8199-8204
Author(s):  
Federico Zocco ◽  
Seán McLoone
Keyword(s):  
Neural Network ◽  
Adaptive Memory ◽  
Neural Network Training ◽  
Network Training

scholarly journals Optimal uncertainty-guided neural network training

2020 ◽  
pp. 106878
Author(s):  
H. M. Dipu Kabir ◽  
Abbas Khosravi ◽  
Abdollah Kavousi-Fard ◽  
Saeid Nahavandi ◽  
Dipti Srinivasan
Keyword(s):  
Neural Network ◽  
Neural Network Training ◽  
Network Training
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