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

Reshma Khemchandani, Anuj Karpatne, Suresh Chandra

Research output: Contribution to journalArticlepeer-review

20 Scopus citations

Abstract

Twin support vector regression (TSVR) determines a pair of ε-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.

Original languageEnglish (US)
Pages (from-to)51-63
Number of pages13
JournalInternational Journal of Machine Learning and Cybernetics
Volume4
Issue number1
DOIs
StatePublished - Jan 21 2013

Keywords

  • Function approximation
  • Support vector regression
  • Twin support vector machines
  • ε-insensitive bound

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