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Least squares support vector machines for direction of arrival estimation

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Please use this identifier to cite or link to this item: http://hdl.handle.net/1928/20289

Least squares support vector machines for direction of arrival estimation

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Title: Least squares support vector machines for direction of arrival estimation
Author: Abdallah, Chaouki T.; Rohwer, Judd A.; Christodoulou, Christos G.
Subject(s): Data mining
Direction of arrival estimation
Least squares approximation
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.
Date: 2003-06-22
Publisher: IEEE
Citation: IEEE Antennas and Propagation Society International Symposium, 2003, 1: 57-60
Description: Digital Object Identifier: 10.1109/APS.2003.1217400
URI: http://hdl.handle.net/1928/20289
ISBN: 0-7803-7846-6

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