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Support vector machines for antenna array processing and electromagnetics

By: Martínez-Ramón, Manel 1968-.
Contributor(s): Christodoulou, Christos G.
Material type: materialTypeLabelBookSeries: Synthesis lectures on computational electromagnetics: #5.Publisher: San Rafael, Calif. (1537 Fourth Street, San Rafael, CA 94901 USA) : Morgan & Claypool Publishers, c2006Edition: 1st ed.Description: 1 electronic text (ix, 110 p. : ill.) : digital file.ISBN: 1598290258 (electronic bk.); 9781598290257 (electronic bk.); 159829024X (pbk.); 9781598290240 (pbk.).Uniform titles: Synthesis digital library of engineering and computer science. Subject(s): Antenna arrays | Electromagnetism | Machine learning | Multivariate analysis | Signal processing -- Statistical methods | Support vector machines | Beamforming | Angle of arrival | Electromagnetics | Antenna arraysDDC classification: 621.3824 Online resources: Abstract with links to resource | Abstract with links to full text Also available in print.
Contents:
1. Introduction -- 1.1. Motivation of this book -- 1.2. Learning machines and generalization -- 1.3. Organization of this book -- 2. Linear support vector machines -- 2.1. An intuitive explanation of the support vector classifier -- 2.2. An intuitive explanation of the support vector regressor -- 3. Nonlinear support vector machines -- 3.1. The Kernel trick -- 3.2. Construction of a nonlinear SVC -- 3.3. Construction of a nonlinear SVR -- 4. Advanced topics -- 4.1. Support vector machines in the complex plane -- 4.2. Linear support vector ARx -- 4.3. Robust cost function of support vector regressors -- 4.4. Parameter selection -- 5. Support vector machines for beamforming -- 5.1. Problem statement -- 5.2. Linear SVM beamformer with temporal reference -- 5.3. Linear SVM beamformer with spatial reference -- 5.4. Nonlinear parameter estimation of linear beamformers -- 5.5. Nonlinear SVM beamformer with temporal reference -- 5.6. Nonlinear SVM beamformer with spatial reference -- 6. Determination of angle of arrival -- 6.1. Linear SVM AOA estimator using regression -- 6.2. Nonlinear AOA estimators -- 6.3. Nonlinear SVM estimator using multiclass classification -- 7. Other applications in electromagnetics -- 7.1. Buried object detection -- 7.2. Sidelobe control -- 7.3. Intelligent alignment of waveguide filters.
Summary: Support vector machines (SVM) were introduced in the early 90's as a novel nonlinear solution for classification and regression tasks. These techniques have been proved to have superior performances in a large variety of real world applications due to their generalization abilities and robustness against noise and interferences.
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Item type Current location Call number Status Date due Barcode Item holds
E books E books PK Kelkar Library, IIT Kanpur
Available EBKE036
Total holds: 0

Mode of access: World Wide Web.

System requirements: Adobe Acrobat Reader.

Part of: Synthesis digital library of engineering and computer science.

Series from website.

Includes bibliographical references (p. 103-108) and index.

1. Introduction -- 1.1. Motivation of this book -- 1.2. Learning machines and generalization -- 1.3. Organization of this book -- 2. Linear support vector machines -- 2.1. An intuitive explanation of the support vector classifier -- 2.2. An intuitive explanation of the support vector regressor -- 3. Nonlinear support vector machines -- 3.1. The Kernel trick -- 3.2. Construction of a nonlinear SVC -- 3.3. Construction of a nonlinear SVR -- 4. Advanced topics -- 4.1. Support vector machines in the complex plane -- 4.2. Linear support vector ARx -- 4.3. Robust cost function of support vector regressors -- 4.4. Parameter selection -- 5. Support vector machines for beamforming -- 5.1. Problem statement -- 5.2. Linear SVM beamformer with temporal reference -- 5.3. Linear SVM beamformer with spatial reference -- 5.4. Nonlinear parameter estimation of linear beamformers -- 5.5. Nonlinear SVM beamformer with temporal reference -- 5.6. Nonlinear SVM beamformer with spatial reference -- 6. Determination of angle of arrival -- 6.1. Linear SVM AOA estimator using regression -- 6.2. Nonlinear AOA estimators -- 6.3. Nonlinear SVM estimator using multiclass classification -- 7. Other applications in electromagnetics -- 7.1. Buried object detection -- 7.2. Sidelobe control -- 7.3. Intelligent alignment of waveguide filters.

Abstract freely available; full-text restricted to subscribers or individual document purchasers.

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Support vector machines (SVM) were introduced in the early 90's as a novel nonlinear solution for classification and regression tasks. These techniques have been proved to have superior performances in a large variety of real world applications due to their generalization abilities and robustness against noise and interferences.

Also available in print.

Title from PDF t.p. (viewed on Oct. 19, 2008).

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