Record Details

Training Algorithms for Supervised Machine Learning: Comparative Study

International Journal of Management and Information Technology

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Field Value
 
Title Training Algorithms for Supervised Machine Learning: Comparative Study
 
Creator Khan, Dr. Rafiqul Zaman
Allamy, Haider
 
Subject artificial neural networks; supervised learning; back propagation; Perceptron; Decision Tree learning algorithm
 
Description 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.
 
Publisher CIRWORLD
 
Date 2009-12-30
 
Type info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Peer-reviewed Article
 
Format application/pdf
 
Identifier https://cirworld.com/index.php/ijmit/article/view/773
 
Source INTERNATIONAL JOURNAL OF MANAGEMENT & INFORMATION TECHNOLOGY; Vol 4 No 3; 354-360
2278-5612
 
Language eng
 
Relation https://cirworld.com/index.php/ijmit/article/view/773/755
 
Rights Copyright (c) 2013 INTERNATIONAL JOURNAL OF MANAGEMENT & INFORMATION TECHNOLOGY
http://creativecommons.org/licenses/by/4.0