Reduced Support Vector Selection by Linear Programs

Authors:
Winfried A. Fellenz
Affiliations:
-
Venue:
IWANN '01 Proceedings of the 6th International Work-Conference on Artificial and Natural Neural Networks: Connectionist Models of Neurons, Learning Processes and Artificial Intelligence-Part I
Year:
2001

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Abstract

The problem of selecting a minimal number of data points required to completely specify a nonlinear sepatating hyperplane classifier is formulated as a concave minimization problem and soloved using a linear program. A comparison of the prediction errors for several rule extraction methods shows a good compromise between complexity of the classifier and the errors.