Abstract
Large margin classifiers are computed to assign patterns to a class with high confidence. This strategy helps controlling the capacity of the learning device so good generalization is presumably achieved. Two recent examples of large margin classifiers are support vector learning machines (SVM) [12] and boosting classifiers [10]. In this paper we show that it is possible to compute large-margin maximum classifiers using a gradient-based learning based on a cost function directly connected with their average margin. We also prove that the use of this procedure in nearest-neighbor (NN) classifiers induce solutions closely related to support vectors.
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Bermejo, S., Cabestany, J. (2001). Large Margin Nearest Neighbor Classifiers. 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_80
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DOI: https://doi.org/10.1007/3-540-45720-8_80
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