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dc.contributor.authorUysal, İ.en_US
dc.contributor.authorGüvenir, H. A.en_US
dc.date.accessioned2018-04-12T13:45:47Z
dc.date.available2018-04-12T13:45:47Zen_US
dc.date.issued1999en_US
dc.identifier.issn0269-8889
dc.identifier.urihttp://hdl.handle.net/11693/38152en_US
dc.description.abstractPredicting 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).en_US
dc.language.isoEnglishen_US
dc.source.titleKnowledge Engineering Reviewen_US
dc.relation.isversionofhttp://dx.doi.org/10.1017/S026988899900404Xen_US
dc.subjectAlgorithmsen_US
dc.subjectComputational Complexityen_US
dc.subjectDatabase Systemsen_US
dc.subjectDistance Measurementen_US
dc.subjectFunctionsen_US
dc.subjectLearning Systemsen_US
dc.subjectNeural networksen_US
dc.subjectRegression Analysisen_US
dc.subjectRoboticsen_US
dc.subjectInstance-Based Regressionen_US
dc.subjectLocally Weighted Regression (LWR)en_US
dc.subjectProjection Pursuit Regression (PPR)en_US
dc.subjectKnowledge Engineeringen_US
dc.titleAn overview of regression techniques for knowledge discoveryen_US
dc.typeReviewen_US
dc.departmentDepartment of Computer Engineeringen_US
dc.citation.spage319en_US
dc.citation.epage340en_US
dc.citation.volumeNumber14en_US
dc.citation.issueNumber4en_US
dc.identifier.doi10.1017/S026988899900404Xen_US
dc.publisherCambridge University Pressen_US
dc.identifier.eissn1469-8005


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