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JACIII Vol.9 No.6 pp. 693-697
doi: 10.20965/jaciii.2005.p0693
(2005)

Paper:

Machine Learning: Automated Knowledge Acquisition Based on Unsupervised Neural Network and Expert System Paradigms

Nazar Elfadil

College of Engineering, Sultan Qaboos University, Muscat, Sultanate of Oman

Received:
October 28, 2003
Accepted:
January 5, 2005
Published:
November 20, 2005
Keywords:
machine learning, auto extraction, neural network, expert system, knowledge acquisition
Abstract

Self-organizing maps are unsupervised neural network models that lend themselves to the cluster analysis of high-dimensional input data. Interpreting a trained map is difficult because features responsible for specific cluster assignment are not evident from resulting map representation. This paper presents an approach to automated knowledge acquisition using Kohonen’s self-organizing maps and k-means clustering. To demonstrate the architecture and validation, a data set representing animal world has been used as the training data set. The verification of the produced knowledge base is done by using conventional expert system.

Cite this article as:
Nazar Elfadil, “Machine Learning: Automated Knowledge Acquisition Based on Unsupervised Neural Network and Expert System Paradigms,” J. Adv. Comput. Intell. Intell. Inform., Vol.9, No.6, pp. 693-697, 2005.
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