A comparative study of the scalability of a sensitivity-based learning algorithm for artificial neural networks

2013 ◽  
Vol 40 (10) ◽  
pp. 3900-3905 ◽  
Author(s):  
Diego Peteiro-Barral ◽  
Bertha Guijarro-Berdiñas ◽  
Beatriz Pérez-Sánchez ◽  
Oscar Fontenla-Romero
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Comparative Study ◽  
Learning Algorithm ◽  
Artificial Neural

scholarly journals Training Algorithms for Supervised Machine Learning: Comparative Study

2013 ◽  
Vol 4 (3) ◽  
pp. 354-360 ◽  
Author(s):  
Dr. Rafiqul Zaman Khan ◽  
Haider Allamy
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Decision Tree ◽  
Comparative Study ◽  
Learning Algorithm ◽  
Supervised Machine Learning ◽  
Back Propagation Algorithm ◽  
Learning Stage ◽  
Artificial Neural

Supervised machine learning is an important task for learning artificial neural networks; therefore a demand for selected supervised learning algorithms such as back propagation algorithm, decision tree learning algorithm and perceptron algorithm has been arise in order to perform the learning stage of the artificial neural networks. In this paper; a comparative study has been presented for the aforementioned algorithms to evaluate their performance within a range of specific parameters such as speed of learning, overfitting avoidance, and their accuracy. Besides these parameters we have included their benefits and limitations to unveil their hidden features and provide more details regarding their performance. We have found the decision tree algorithm is the best as compared with other algorithms that can solve the complex problems with a remarkable speed.

scholarly journals Reconstruction of Storm‐Time Total Electron Content Using Ionospheric Tomography and Artificial Neural Networks: A Comparative Study Over the African Region

Radio Science ◽  
2018 ◽  
Vol 53 (11) ◽  
pp. 1328-1345 ◽  
Author(s):  
Jean Claude Uwamahoro ◽  
Nigussie M. Giday ◽  
John Bosco Habarulema ◽  
Zama T. Katamzi‐Joseph ◽  
Gopi Krishna Seemala
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Comparative Study ◽  
Total Electron Content ◽  
Electron Content ◽  
African Region ◽  
Total Electron ◽  
Ionospheric Tomography ◽  
Artificial Neural
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