Hostname: page-component-78c5997874-m6dg7 Total loading time: 0 Render date: 2024-11-06T10:41:47.606Z Has data issue: false hasContentIssue false
  • English
  • Français

An overview of regression techniques for knowledge discovery

Published online by Cambridge University Press:  01 December 1999

İLHAN UYSAL  and
H. ALTAY GÜVENIR
İLHAN UYSAL
Affiliation:
Department of Computer Engineering and Information Sciences, Bilkent University, 06533 Ankara, Turkey (email: [email protected], [email protected])
H. ALTAY GÜVENIR
Affiliation:
Department of Computer Engineering and Information Sciences, Bilkent University, 06533 Ankara, Turkey (email: [email protected], [email protected])
Get access Rights & Permissions [Opens in a new window]

Abstract

Predicting or learning numeric features is called regression in the statistical literature, and it is the subject of research in both machine learning and statistics. This paper reviews the important techniques and algorithms for regression developed by both communities. Regression is important for many applications, since lots of real life problems can be modeled as regression problems. The review includes Locally Weighted Regression (LWR), rule-based regression, Projection Pursuit Regression (PPR), instance-based regression, Multivariate Adaptive Regression Splines (MARS) and recursive partitioning regression methods that induce regression trees (CART, RETIS and M5).

Type
Review Article
Information
The Knowledge Engineering Review , Volume 14 , Issue 4 , December 1999 , pp. 319 - 340
Copyright
© 1999 Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

This research is supported, in part, by TUBITAK (Scientific and Technical Research Council of Turkey) under Grant 198E015.