Support Vector Machines (SVM) are known as the state of the art learning algorithms. Although they are called as the state of the art learning algorithms, the kernel functions take an important place when the SVM performance is the main concern. Recently Chebyshev kernel has been proposed as a good alternative to the well known and used Gaussian kernel. Therefore in this book we studied this kernel functions as well as several other kernel functions, and compared their performances. We also proposed a generalization method to use Chebyshev kernels on vector inputs directly. Moreover, we also discussed some properties of Gaussian kernels when used with SVM.
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Sedat Ozer obtained his B.Sc. degree in electronics engineering from Istanbul University, Turkey and his M.Sc degree in electrical engineering from University of Massachusetts, Dartmouth,(UMASD) MA. Currently he is a PhD candidate in electrical engineering at Rutgers University, NJ and working on machine learning, computer vision problems.
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Taschenbuch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Support Vector Machines (SVM) are known as the state of the art learning algorithms. Although they are called as the state of the art learning algorithms, the kernel functions take an important place when the SVM performance is the main concern. Recently Chebyshev kernel has been proposed as a good alternative to the well known and used Gaussian kernel. Therefore in this book we studied this kernel functions as well as several other kernel functions, and compared their performances. We also proposed a generalization method to use Chebyshev kernels on vector inputs directly. Moreover, we also discussed some properties of Gaussian kernels when used with SVM. 84 pp. Englisch. Bestandsnummer des Verkäufers 9783838305578
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Taschenbuch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Support Vector Machines (SVM) are known as the state of the art learning algorithms. Although they are called as the state of the art learning algorithms, the kernel functions take an important place when the SVM performance is the main concern. Recently Chebyshev kernel has been proposed as a good alternative to the well known and used Gaussian kernel. Therefore in this book we studied this kernel functions as well as several other kernel functions, and compared their performances. We also proposed a generalization method to use Chebyshev kernels on vector inputs directly. Moreover, we also discussed some properties of Gaussian kernels when used with SVM. Bestandsnummer des Verkäufers 9783838305578
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Zustand: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Ozer SedatSedat Ozer obtained his B.Sc. degree in electronics engineering from Istanbul University, Turkey and his M.Sc degree in electrical engineering from University of Massachusetts, Dartmouth,(UMASD) MA. Currently he is a PhD ca. Bestandsnummer des Verkäufers 5411281
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Taschenbuch. Zustand: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Support Vector Machines (SVM) are known as the state of the art learning algorithms. Although they are called as the state of the art learning algorithms, the kernel functions take an important place when the SVM performance is the main concern. Recently Chebyshev kernel has been proposed as a good alternative to the well known and used Gaussian kernel. Therefore in this book we studied this kernel functions as well as several other kernel functions, and compared their performances. We also proposed a generalization method to use Chebyshev kernels on vector inputs directly. Moreover, we also discussed some properties of Gaussian kernels when used with SVM.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 84 pp. Englisch. Bestandsnummer des Verkäufers 9783838305578
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Taschenbuch. Zustand: Neu. A kernel study on Support Vector Machines | Generalized Chebyshev Kernels | Sedat Ozer (u. a.) | Taschenbuch | 84 S. | Englisch | 2010 | LAP LAMBERT Academic Publishing | EAN 9783838305578 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. Bestandsnummer des Verkäufers 101507884
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