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Evaluating the Generalization Ability of Support Vector Machines through the Bootstrap

โœ Scribed by Davide Anguita; Andrea Boni; Sandro Ridella


Book ID
110277598
Publisher
Springer US
Year
2000
Tongue
English
Weight
73 KB
Volume
11
Category
Article
ISSN
1370-4621

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The antenna design is a complicated and time-consuming procedure. This work explores using support vector machines (SVMs), a statistical learning theory based on the structural risk minimization principle and has a great generalization capability, as a fast and accurate tool in the antenna design. A