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Author: Peyman Kabiri

Affiliation: College of Computer Engineering, Iran’s University of Science and Technology, Iran, Islamic Republic of

Keyword(s): Machine Learning, Data Visualisation and Approximation.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Artificial Intelligence and Decision Support Systems ; Biomedical Engineering ; Biomedical Signal Processing ; Computational Intelligence ; Enterprise Information Systems ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Industrial Applications of Artificial Intelligence ; Methodologies and Methods ; Neural Network Software and Applications ; Neural Networks ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Signal Processing ; Soft Computing ; Theory and Methods

Abstract: The Proportional Keen Approximation method is a young learning method using the linear approximation to learn hypothesis. In the paper this methodology will be compared with another well-established learning method i.e. the Artificial Neural Networks. The aim of this comparison is to learn about the strengths and the weaknesses of these learning methods regarding different properties of their learning process. The comparison is made using two different comparison methods. In the first method the algorithm and the known behavioural model of these methods are analysed. Later, using this analysis, these methods are compared. In the second approach, a reference dataset that contains some of the most problematic features in the learning process is selected. Using the selected dataset the differences between two learning methods are numerically analysed and a comparison is made.

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Paper citation in several formats:
Kabiri, P. (2004). A COMPARISON BETWEEN THE PROPORTIONAL KEEN APPROXIMATOR AND THE NEURAL NETWORKS LEARNING METHODS. In Proceedings of the Sixth International Conference on Enterprise Information Systems - Volume 2: ICEIS; ISBN 972-8865-00-7; ISSN 2184-4992, SciTePress, pages 159-164. DOI: 10.5220/0002599401590164

@conference{iceis04,
author={Peyman Kabiri.},
title={A COMPARISON BETWEEN THE PROPORTIONAL KEEN APPROXIMATOR AND THE NEURAL NETWORKS LEARNING METHODS},
booktitle={Proceedings of the Sixth International Conference on Enterprise Information Systems - Volume 2: ICEIS},
year={2004},
pages={159-164},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002599401590164},
isbn={972-8865-00-7},
issn={2184-4992},
}

TY - CONF

JO - Proceedings of the Sixth International Conference on Enterprise Information Systems - Volume 2: ICEIS
TI - A COMPARISON BETWEEN THE PROPORTIONAL KEEN APPROXIMATOR AND THE NEURAL NETWORKS LEARNING METHODS
SN - 972-8865-00-7
IS - 2184-4992
AU - Kabiri, P.
PY - 2004
SP - 159
EP - 164
DO - 10.5220/0002599401590164
PB - SciTePress

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