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Support Vector Machines for Antenna Array Processing and Electromagnetics [electronic resource] / by Manel Martínez-Ramón, Christos Christodoulou.

By: Martínez-Ramón, Manel [author.].
Contributor(s): Christodoulou, Christos [author.] | SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Synthesis Lectures on Computational Electromagnetics: Publisher: Cham : Springer International Publishing : Imprint: Springer, 2006Edition: 1st ed. 2006.Description: IX, 110 p. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783031016929.Subject(s): Engineering | Electrical engineering | Telecommunication | Technology and Engineering | Electrical and Electronic Engineering | Microwaves, RF Engineering and Optical CommunicationsAdditional physical formats: Printed edition:: No title; Printed edition:: No titleDDC classification: 620 Online resources: Click here to access online
Contents:
Introduction -- Linear Support Vector Machines -- Nonlinear Support Vector Machines -- Advanced Topics -- Support Vector Machines for Beamforming -- Determination of Angle of Arrival -- Other Applications in Electromagnetics.
In: Springer Nature eBookSummary: 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. This book introduces a set of novel techniques based on SVM that are applied to antenna array processing and electromagnetics. In particular, it introduces methods for linear and nonlinear beamforming and parameter design for arrays and electromagnetic applications.
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Introduction -- Linear Support Vector Machines -- Nonlinear Support Vector Machines -- Advanced Topics -- Support Vector Machines for Beamforming -- Determination of Angle of Arrival -- Other Applications in Electromagnetics.

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. This book introduces a set of novel techniques based on SVM that are applied to antenna array processing and electromagnetics. In particular, it introduces methods for linear and nonlinear beamforming and parameter design for arrays and electromagnetic applications.

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