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International Journal of Machine Learning and Cybernetics
Erschienen in: International Journal of Machine Learning and Cybernetics 1/2013

01.02.2013 | Original Article

Twin support vector regression for the simultaneous learning of a function and its derivatives

verfasst von: Reshma Khemchandani, Anuj Karpatne, Suresh Chandra

Erschienen in: International Journal of Machine Learning and Cybernetics | Ausgabe 1/2013

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Abstract

Twin support vector regression (TSVR) determines a pair of \(\epsilon\)-insensitive up- and down-bound functions by solving two related support vector machine-type problems, each of which is smaller than that in a classical SVR. On the lines of TSVR, we have proposed a novel regressor for the simultaneous learning of a function and its derivatives, termed as TSVR of a Function and its Derivatives. Results over several functions of more than one variable demonstrate its effectiveness over other existing approaches in terms of improving the estimation accuracy and reducing run time complexity.
Vorheriger Artikel Efficient sub-window search with fixed shape sub-windows
Nächster Artikel Capacitated two-stage facility location problem with fuzzy costs and demands

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Metadaten
Titel
Twin support vector regression for the simultaneous learning of a function and its derivatives
verfasst von
Reshma Khemchandani
Anuj Karpatne
Suresh Chandra
Publikationsdatum
01.02.2013
Verlag
Springer-Verlag
Erschienen in
International Journal of Machine Learning and Cybernetics / Ausgabe 1/2013
Print ISSN: 1868-8071
Elektronische ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-012-0072-1

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