Journal
NONLINEAR ANALYSIS-THEORY METHODS & APPLICATIONS
Volume 73, Issue 9, Pages 2855-2867Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.na.2010.06.035
Keywords
SVM; Regression; Uncertainty management; Relevance-based learning
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Funding
- PASCAL2 Network of Excellence under EC [216886]
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We propose a variant of two SVM regression algorithms expressly tailored in order to exploit additional information summarizing the relevance of each data item, as a measure of its relative importance w.r.t. the remaining examples. These variants, enclosing the original formulations when all data items have the same relevance, are preliminary tested on synthetic and real-world data sets. The obtained results outperform standard SVM approaches to regression if evaluated in light of the above mentioned additional information about data quality. (C) 2010 Elsevier Ltd. All rights reserved.
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