Proceedings

An Empirical Comparison of Pattern Recognition, Neural Nets, and Machine Learning Classification Methods

Sholom M. Weiss, Ioannis Kapouleas

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Proceedings of the Eleventh International Joint Conference on Artificial Intelligence. Machine Learning. Pages 781-786.

Abstract

Classification methods from statistical pattern recognition, neural nets, and machine learning were apphed to four real-world data sets. Each of these data sets has been previously analyzed and reported in the statistical, medical, or machine learning literature. The data sets are characterized by statistical uncertainty; there is no completely accurate solution to these problems. Training and testing or resampling techniques are used to estimate the true error rates of the classification methods. Detailed attention is given to the analysis of performance of the neural nets using back propagation. For these problems, which have relatively few hypotheses and features, the machine learning procedures for rule induction or tree induction clearly performed best.

Sholom M. Weiss, and Ioannis Kapouleas. 1989. An Empirical Comparison of Pattern Recognition, Neural Nets, and Machine Learning Classification Methods. In Proceedings of the Eleventh International Joint Conference on Artificial Intelligence, IJCAI-89. IJCAI, 781–786. https://ijcai.org/proceedings/1989/125
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