Adaptive Local Hyperplane for Regression Tasks

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dc.contributor.author Kecman, Vojislav en_US
dc.contributor.author Yang, Tao en_US
dc.contributor.editor Robert Kozma en_US
dc.date.accessioned 2010-07-13T08:50:15Z
dc.date.available 2010-07-13T08:50:15Z
dc.date.issued 2009 en_US
dc.identifier 2009002173 en_US
dc.identifier.citation Kecman Vojislav and Yang Tao 2009, 'Adaptive Local Hyperplane for Regression Tasks', International Neural Network Society, USA, pp. 1566-1570. en_US
dc.identifier.issn 978-1-4244-3548-7 en_US
dc.identifier.other E1UNSUBMIT en_US
dc.identifier.uri http://hdl.handle.net/10453/12637
dc.description.abstract The paper introduces novel machine learning (data mining) algorithm called Adaptive Local Hyperplane (ALH) and it presents its application in solving regression problems. ALH algorithm has recently shown extremely good results in classification, and it is adopted for solving regression tasks here. It is a local margin maximizing algorithm in the original, weighted, input space blending a Nearest Neighbors (NN) based approaches and Support Vector Machines (SVMs) ideas about the maximal margin. In performing such a task it uses only K closest points to the query data point. Results for four benchmarking regression data sets show superior performance to SVMs as well as to the other established regression methods en_US
dc.language en_US
dc.publisher International Neural Network Society en_US
dc.relation.isbasedon http://dx.doi.org/10.1109/IJCNN.2009.5178919 en_US
dc.title Adaptive Local Hyperplane for Regression Tasks en_US
dc.parent International Joint Conference on Neural Networks (IJCNN) en_US
dc.journal.volume 1 en_US
dc.journal.number en_US
dc.publocation USA en_US
dc.identifier.startpage 1566 en_US
dc.identifier.endpage 1570 en_US
dc.cauo.name QCIS Investment Core en_US
dc.conference Verified OK en_US
dc.for 080100 en_US
dc.personcode 0000059430 en_US
dc.personcode 108195 en_US
dc.percentage 100 en_US
dc.classification.name Artificial Intelligence and Image Processing en_US
dc.classification.type FOR-08 en_US
dc.edition en_US
dc.custom IEEE International Joint Conference on Neural Networks en_US
dc.date.activity 20090614 en_US
dc.location.activity Atlanta, GA, USA en_US
dc.description.keywords data mining , learning (artificial intelligence) , mathematics computing , optimisation , pattern classification , regression analysis , support vector machines en_US
dc.staffid en_US
dc.staffid 108195 en_US


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