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Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/15373

Title: CSLS: Connectionist Symbolic Learning System
Authors: Mehmet Sabih Aksoy
Hassan Mathkour
Keywords: Inductive learning; neural networks; symbiotic systems.
Issue Date: 2009
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.
URI: http://hdl.handle.net/123456789/15373
Appears in Collections:College of Computer and Information Sciences

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