Support vector machines with example dependent costs

Publikation: Beiträge in ZeitschriftenKonferenzaufsätze in FachzeitschriftenForschungbegutachtet

Authors

Classical 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 present a natural cost-sensitive extension of the support vector machine (SVM) and discuss its relation to the Bayes rule. We also derive an approach for including example dependent costs into an arbitrary cost-insensitive learning algorithm by sampling according to modified probability distributions.
OriginalspracheEnglisch
ZeitschriftLecture Notes in Computer Science
Jahrgang2837
Seiten (von - bis)23-34
Anzahl der Seiten12
ISSN0302-9743
DOIs
PublikationsstatusErschienen - 01.01.2003
Extern publiziertJa

DOI

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