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1999 Volume 14
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RESEARCH ARTICLE   Open Access    

An overview of regression techniques for knowledge discovery

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  • 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).
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    İLHAN UYSAL, H. ALTAY GÜVENIR. 1999. An overview of regression techniques for knowledge discovery. The Knowledge Engineering Review. 14: doi: 10.1017/S026988899900404X
    İLHAN UYSAL, H. ALTAY GÜVENIR. 1999. An overview of regression techniques for knowledge discovery. The Knowledge Engineering Review. 14: doi: 10.1017/S026988899900404X
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RESEARCH ARTICLE   Open Access    

An overview of regression techniques for knowledge discovery

  • Published online: 01 December 1999
The Knowledge Engineering Review  14,  Article number: 10.1017/S026988899900404X  (1999)  |  Cite this article

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).

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    İLHAN UYSAL, H. ALTAY GÜVENIR. 1999. An overview of regression techniques for knowledge discovery. The Knowledge Engineering Review. 14: doi: 10.1017/S026988899900404X
    İLHAN UYSAL, H. ALTAY GÜVENIR. 1999. An overview of regression techniques for knowledge discovery. The Knowledge Engineering Review. 14: doi: 10.1017/S026988899900404X
    shu

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