An experimental comparison of symbolic and neural learning algorithms

Comparative strengths and weaknesses of symbolic and neural learning algorithms are analysed. Experiments comparing the new generation symbolic algorithms and neural network algorithms have been performed using twelve large, real-world data sets. Results indicate that their performances are comparab...

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Bibliographic Details
Published in1998 Second International Conference on Knowledge-Based Intelligent Engineering Systems Proceedings Vol. 2; pp. 306 - 315 vol.2
Main Authors Baykal, N., Tolun, M.R.
Format Conference Proceeding
LanguageEnglish
Published IEEE 1998
Subjects
Accuracy
Algorithm design and analysis
Backpropagation algorithms
Cardiac disease
Decision trees
Machine learning algorithms
Neural networks
Performance analysis
Prediction algorithms
Robustness
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ISBN9780780343160
0780343166
DOI10.1109/KES.1998.725927

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Summary:Comparative strengths and weaknesses of symbolic and neural learning algorithms are analysed. Experiments comparing the new generation symbolic algorithms and neural network algorithms have been performed using twelve large, real-world data sets. Results indicate that their performances are comparable for most of the different data sets. However, in some data sets neural network algorithms' predicted accuracies are statistically significant than symbolic algorithms and in others symbolic algorithms' performances are superior. In general, neural network algorithms are found quite robust when noisy and missing data are introduced in the data sets.
ISBN:9780780343160
0780343166
DOI:10.1109/KES.1998.725927