Learning linear classifiers sensitive to example dependent and noisy costs

Publikation: Beiträge in ZeitschriftenZeitschriftenaufsätzeForschungbegutachtet

Authors

Learning algorithms from the fields of artificial neural networks and machine learning, typically, do not take any costs into account or allow only costs depending on the classes of the examples that are used for learning. As an extension of class dependent costs, we consider costs that are example, i.e. feature and class dependent. We derive a cost-sensitive perceptron learning rule for non-separable classes, that can be extended to multi-modal classes (DIPOL) and present a natural cost-sensitive extension of the support vector machine (SVM).
OriginalspracheEnglisch
ZeitschriftLecture Notes in Computer Science
Jahrgang2810
Seiten (von - bis)167-178
Anzahl der Seiten12
ISSN0302-9743
DOIs
PublikationsstatusErschienen - 01.01.2003
Extern publiziertJa

DOI

Zuletzt angesehen

Forschende

  1. Andreas Bernard

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