Support vector machines with example dependent costs

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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.

Original languageEnglish
JournalLecture Notes in Computer Science
Volume2837
Pages (from-to)23-34
Number of pages12
ISSN0302-9743
DOIs
Publication statusPublished - 01.01.2003
Externally publishedYes

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