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      An overview of regression techniques for knowledge discovery

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      Author
      Uysal, İ.
      Güvenir, H. A.
      Date
      1999
      Source Title
      Knowledge Engineering Review
      Print ISSN
      0269-8889
      Electronic ISSN
      1469-8005
      Publisher
      Cambridge University Press
      Volume
      14
      Issue
      4
      Pages
      319 - 340
      Language
      English
      Type
      Review
      Item Usage Stats
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      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).
      Keywords
      Algorithms
      Computational Complexity
      Database Systems
      Distance Measurement
      Functions
      Learning Systems
      Neural networks
      Regression Analysis
      Robotics
      Instance-Based Regression
      Locally Weighted Regression (LWR)
      Projection Pursuit Regression (PPR)
      Knowledge Engineering
      Permalink
      http://hdl.handle.net/11693/38152
      Published Version (Please cite this version)
      http://dx.doi.org/10.1017/S026988899900404X
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      • Department of Computer Engineering 1419
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