Title: Interval discriminant analysis using Support Vector Machines
Abstract: Imprecision, incompleteness, prior knowledge or improved learning speed can motivate interval-represented data. Most approaches for SVM learning of interval data use local kernels based on interval dis- tances. We present here a novel approach, suitable for linear SVMs, which allows to deal with interval data without resorting to interval distances. The experimental results confirms the validity of our proposal.
Publication Year: 2007
Publication Date: 2007-01-01
Language: en
Type: article
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Cited By Count: 7
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