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Math. Comput. Appl. 2009, 14(3), 177-186; doi:10.3390/mca14030177

CSLS: Connectionist Symbolic Learning System

1
Department of Information Systems King Saud University Riyadh 11543, Saudi Arabia
2
Department of Computer Science King Saud University Riyadh 11543, Saudi Arabia
*
Author to whom correspondence should be addressed.
Published: 1 December 2009
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Abstract

This paper presents CSLS, a symbiotic combination of inductive and neural learning. CSLS has two components, an induction algorithm to carry out inductive learning and a multi-layer perceptron (MLP) to implement neural learning. The paper outlines the operation of the components of CSLS and describes how the combined system is designed to utilise the individual strengths of inductive and neural learning to the best advantage. The paper gives the results of evaluating CSLS on the IRIS data and Breast-Cancer-Wisconsin-data classification problems. These clearly demonstrate the main benefit of the symbiotic combination: the combined system performs better than either of its components.
Keywords: Inductive learning; neural networks; symbiotic systems Inductive learning; neural networks; symbiotic systems
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Aksoy, M.S.; Mathkou, H. CSLS: Connectionist Symbolic Learning System. Math. Comput. Appl. 2009, 14, 177-186.

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Math. Comput. Appl. EISSN 2297-8747 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
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