Perceptron and SVM learning with generalized cost models

Publikation: Beiträge in ZeitschriftenZeitschriftenaufsätzeForschungbegutachtet

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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). 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
ZeitschriftIntelligent Data Analysis
Jahrgang8
Ausgabenummer5
Seiten (von - bis)439-455
Anzahl der Seiten17
ISSN1088-467X
DOIs
PublikationsstatusErschienen - 2004
Extern publiziertJa

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