Abstract
Several instance-based large-margin classifiers have recently been put forward in the literature: Support Hyperplanes, Nearest Convex Hull classifier and Soft Nearest Neighbor. We examine those techniques from a common fit-versuscomplexity framework and study the links between them. Finally, we compare the performance of these techniques vis-a-vis each other and other standard classification methods.
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Nalbantov, G., Groenen, P., Smirnov, E. (2012). A Comparative Analysis of Instance-based Penalization Techniques for Classification. In: Dai, H., Liu, J., Smirnov, E. (eds) Reliable Knowledge Discovery. Springer, Boston, MA. https://doi.org/10.1007/978-1-4614-1903-7_13
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DOI: https://doi.org/10.1007/978-1-4614-1903-7_13
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