Perceptron and SVM learning with generalized cost models

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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.

Original languageEnglish
JournalIntelligent Data Analysis
Volume8
Issue number5
Pages (from-to)439-455
Number of pages17
ISSN1088-467X
DOIs
Publication statusPublished - 2004
Externally publishedYes

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Researchers

  1. Pierangelo Maset

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