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Cluster Computing

Erschienen in:

19.02.2020

A density-based maximum margin machine classifier

verfasst von: Jinsong Wang, Jiping Liao, Wei Huang

Erschienen in: Cluster Computing | Ausgabe 4/2020

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Abstract

Classic support vector machine classifiers find separating hyperplanes by considering patterns of data sets, such as so-called support vectors without any character, i.e., without any global information concerning the relationship between one point and other points. In this study, we propose a density-based maximum margin machine classifier based on the idea of replacing support vectors with edge-points. Each edge-point of a data set is characterized by a density that represents the distance between the point and its neighbours. In some sense, the density character of a pattern (edge-point) is used here as global information relation the pattern to other points. To evaluate the performance of the proposed approach, we test it on several benchmark data sets. A comparative study demonstrates the advantages of our new approach.

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Titel: A density-based maximum margin machine classifier
verfasst von: Jinsong Wang
Jiping Liao
Wei Huang
Publikationsdatum: 19.02.2020
Verlag: Springer US
Erschienen in: Cluster Computing / Ausgabe 4/2020
Print ISSN: 1386-7857
Elektronische ISSN: 1573-7543
DOI: https://doi.org/10.1007/s10586-020-03070-w

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