Adaptive Local Hyperplane for Regression Tasks

Kecman, V; Yang, T

Adaptive Local Hyperplane for Regression Tasks

Kecman, V Yang, T

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Publisher:: International Neural Network Society
Publication Type:: Conference Proceeding
Citation:: International Joint Conference on Neural Networks (IJCNN), 2009, 1 pp. 1566 - 1570
Issue Date:: 2009-01

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Field	Value	Language
dc.contributor.author	Kecman, V	en_US
dc.contributor.author	Yang, T	en_US
dc.contributor.editor	Robert Kozma	en_US
dc.date	2009-06-14	en_US
dc.date.issued	2009-01	en_US
dc.identifier.citation	International Joint Conference on Neural Networks (IJCNN), 2009, 1 pp. 1566 - 1570	en_US
dc.identifier.isbn	978-1-4244-3548-7	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.publisher	International Neural Network Society	en_US
dc.relation.ispartof	International Joint Conference on Neural Networks (IJCNN)	en_US
dc.relation.ispartof	IEEE International Joint Conference on Neural Networks	en_US
dc.relation.isbasedon	10.1109/IJCNN.2009.5178919	en_US
dc.title	Adaptive Local Hyperplane for Regression Tasks	en_US
dc.type	Conference Proceeding
utslib.citation.volume	1	en_US
utslib.location	USA	en_US
utslib.location.activity	Atlanta, GA, USA	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	Atlanta, GA, USA	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access
pubs.consider-herdc	false	en_US
pubs.place-of-publication	USA	en_US
pubs.start-date	2009-06-14	en_US
pubs.volume	1	en_US

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

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http://hdl.handle.net/10453/12637