Encyclopedia of Machine Learning and Data Mining

2017 Edition
| Editors: Claude Sammut, Geoffrey I. Webb

Regression

  • Novi QuadriantoEmail author
  • Wray L. Buntine
Reference work entry
DOI: https://doi.org/10.1007/978-1-4899-7687-1_716

Definition

Regression is a fundamental problem in statistics and machine learning. In regression studies, we are typically interested in inferring a real-valued function (called a regression function) whose values correspond to the mean of a dependent (or response or output) variable conditioned on one or more independent (or input) variables. Many different techniques for estimating this regression function have been developed, including parametric, semi-parametric, and nonparametric methods.

Motivation and Background

Assume that we are given a set of data points sampled from an underlying but unknown distribution, each of which includes input x and output y. An example is given in Fig.  1. The task of regression is to learn a hidden functional relationship between x and y from observed and possibly noisy data points. In Fig.  1, the input–output relationship is a Gaussian-corrupted sinusoidal relationship, that is, \(y =\mathrm{ sin}(2\pi x)+\epsilon\)
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Recommended Reading1

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Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.Department of Informatics, SMiLe CLiNiCUniversity of SussexBrightonUK
  2. 2.Statistical Machine Learning ProgramNICTACanberraAustralia
  3. 3.Faculty of Information TechnologyMonash UniversityClaytonAustralia