Adaptive Local Hyperplane for regression tasks

Author

Kecman, Vojislav ; Yang, Tao

Author_Institution

Comput. Sci. Dept., Virginia Commonwealth Univ., Richmond, NJ, USA

fYear

2009

fDate

14-19 June 2009

Firstpage

1566

Lastpage

1570

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.

Keywords

data mining; learning (artificial intelligence); mathematics computing; optimisation; pattern classification; regression analysis; support vector machines; K closest point; adaptive local hyperplane; classification task; data mining; local margin maximizing algorithm; machine learning algorithm; nearest neighbor; regression task; support vector machine; Classification tree analysis; Data mining; Face recognition; Function approximation; Machine learning; Machine learning algorithms; Nearest neighbor searches; Neural networks; Support vector machine classification; Support vector machines;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 2009. IJCNN 2009. International Joint Conference on

Conference_Location

Atlanta, GA

ISSN

1098-7576

Print_ISBN

978-1-4244-3548-7

Electronic_ISBN

1098-7576

Type

conf

DOI

10.1109/IJCNN.2009.5178919

Filename

5178919