Abstract
The problem of selecting a minimal number of data points required to completely specify a nonlinear separating hyperplane classifier is formulated as a concave minimization problem and solved 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.
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Fellenz, W.A. (2001). Reduced Support Vector Selection by Linear Programs. In: Mira, J., Prieto, A. (eds) Connectionist Models of Neurons, Learning Processes, and Artificial Intelligence. IWANN 2001. Lecture Notes in Computer Science, vol 2084. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45720-8_81
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DOI: https://doi.org/10.1007/3-540-45720-8_81
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