Electrical & Computer Engineering Faculty Publications

Document Type

Article

Publication Date

6-22-2003

Abstract

Machine learning research has largely been devoted to binary and multiclass problems relating to data mining, text categorization, and pattern/facial recognition. Recently, popular machine learning algorithms, including support vector machines (SVM), have successfully been applied to wireless communication problems. The paper presents a multiclass least squares SVM (LS-SVM) architecture for direction of arrival (DOA) estimation as applied to a CDMA cellular system. Simulation results show a high degree of accuracy, as related to the DOA classes, and prove that the LS-SVM DDAG (decision directed acyclic graph) system has a wide range of performance capabilities. The multilabel capability for multiple DOAs is discussed. Multilabel classification is possible with the LS-SVM DDAG algorithm presented.

Publisher

IEEE

Publication Title

IEEE Antennas and Propagation Society International Symposium

ISSN

0-7803-7846-6

First Page

57

Last Page

60

DOI

10.1109/APS.2003.1217400

Language (ISO)

English

Sponsorship

IEEE

Keywords

Data mining, Direction of arrival estimation, Least squares approximation

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