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.
Relevance regression learning with support vector machines / B. Apolloni, D. Malchiodi, L. Valerio. - In: NONLINEAR ANALYSIS. - ISSN 0362-546X. - 73:9(2010 Nov), pp. 2855-2867. [10.1016/j.na.2010.06.035]
Relevance regression learning with support vector machines
B. ApolloniPrimo
;D. MalchiodiSecondo
;L. ValerioUltimo
2010
Abstract
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.Pubblicazioni consigliate
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