Support vector machines theory (17 Ergebnisse)
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Zustand: Good. Volume 1. This is an ex-library book and may have the usual library/used-book markings inside.This book has hardback covers. In good all round condition. No dust jacket. Please note the Image in this listing is a stock photo and may not match the covers of the actual item,700grams, ISBN:9789814324380.
- Softcover
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Zustand: New. In.
Verlag: Kluwer Academic Publishers, 2001
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Hardcover. Good condition. Dog-eared corners. Libro.
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Zustand: New. In.
- Hardcover
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EUR 179,57
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Zustand: New. pp. x + 431 Illus.
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Zustand: Hervorragend. Zustand: Hervorragend | Seiten: 320 | Sprache: Englisch | Produktart: Bücher | This book contains select chapters on support vector algorithms from different perspectives, including mathematical background, properties of various kernel functions, and several applications. The main focus of this book is on…orthogonal kernel functions, and the properties of the classical kernel functions¿Chebyshev, Legendre, Gegenbauer, and Jacobi¿are reviewed in some chapters. Moreover, the fractional form of these kernel functions is introduced in the same chapters, and for ease of use for these kernel functions, a tutorial on a Python package named ORSVM is presented. The book also exhibits a variety of applications for support vector algorithms, and in addition to the classification, these algorithms along with the introduced kernel functions are utilized for solving ordinary, partial, integro, and fractional differential equations.On the other hand, nowadays, the real-time and big data applications of support vector algorithms are growing. Consequently, the Compute Unified Device Architecture (CUDA) parallelizing the procedure of support vector algorithms based on orthogonal kernel functions is presented. The book sheds light on how to use support vector algorithms based on orthogonal kernel functions in different situations and gives a significant perspective to all machine learning and scientific machine learning researchers all around the world to utilize fractional orthogonal kernel functions in their pattern recognition or scientific computing problems.
- Softcover
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EUR 131,10
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Taschenbuch. Zustand: Neu. Learning with Fractional Orthogonal Kernel Classifiers in Support Vector Machines | Theory, Algorithms and Applications | Jamal Amani Rad (u. a.) | Taschenbuch | xiv | Englisch | 2024 | Springer | EAN 9789811965555 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heid…elberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
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Zustand: Hervorragend. Zustand: Hervorragend | Seiten: 319 | Sprache: Englisch | Produktart: Bücher | This book contains select chapters on support vector algorithms from different perspectives, including mathematical background, properties of various kernel functions, and several applications. The main focus of this book is on…orthogonal kernel functions, and the properties of the classical kernel functions¿Chebyshev, Legendre, Gegenbauer, and Jacobi¿are reviewed in some chapters. Moreover, the fractional form of these kernel functions is introduced in the same chapters, and for ease of use for these kernel functions, a tutorial on a Python package named ORSVM is presented. The book also exhibits a variety of applications for support vector algorithms, and in addition to the classification, these algorithms along with the introduced kernel functions are utilized for solving ordinary, partial, integro, and fractional differential equations.On the other hand, nowadays, the real-time and big data applications of support vector algorithms are growing. Consequently, the Compute Unified Device Architecture (CUDA) parallelizing the procedure of support vector algorithms based on orthogonal kernel functions is presented. The book sheds light on how to use support vector algorithms based on orthogonal kernel functions in different situations and gives a significant perspective to all machine learning and scientific machine learning researchers all around the world to utilize fractional orthogonal kernel functions in their pattern recognition or scientific computing problems.
- Softcover
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Taschenbuch. Zustand: Neu. Support Vector Machines: Theory and Applications | Lipo Wang | Taschenbuch | Studies in Fuzziness and Soft Computing | x | Englisch | 2010 | Springer | EAN 9783642063688 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]…com | Anbieter: preigu.
- Softcover
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EUR 157,86
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Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book contains select chapters on support vector algorithms from different perspectives, including mathematical background, properties of various kernel functions, and several applications. The main focus of this book is on orthogonal kernel func…tions, and the properties of the classical kernel functions-Chebyshev, Legendre, Gegenbauer, and Jacobi-are reviewed in some chapters. Moreover, the fractional form of these kernel functions is introduced in the same chapters, and for ease of use for these kernel functions, a tutorial on a Python package named ORSVM is presented. The book also exhibits a variety of applications for support vector algorithms, and in addition to the classification, these algorithms along with the introduced kernel functions are utilized for solving ordinary, partial, integro, and fractional differential equations.On the other hand, nowadays, the real-time and big data applications of support vector algorithms are growing. Consequently, the Compute Unified Device Architecture (CUDA) parallelizing the procedure of support vector algorithms based on orthogonal kernel functions is presented. The book sheds light on how to use support vector algorithms based on orthogonal kernel functions in different situations and gives a significant perspective to all machine learning and scientific machine learning researchers all around the world to utilize fractional orthogonal kernel functions in their pattern recognition or scientific computing problems.
- Hardcover
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EUR 157,86
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Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book contains select chapters on support vector algorithms from different perspectives, including mathematical background, properties of various kernel functions, and several applications. The main focus of this book is on orthogonal kernel functions,…and the properties of the classical kernel functions-Chebyshev, Legendre, Gegenbauer, and Jacobi-are reviewed in some chapters. Moreover, the fractional form of these kernel functions is introduced in the same chapters, and for ease of use for these kernel functions, a tutorial on a Python package named ORSVM is presented. The book also exhibits a variety of applications for support vector algorithms, and in addition to the classification, these algorithms along with the introduced kernel functions are utilized for solving ordinary, partial, integro, and fractional differential equations.On the other hand, nowadays, the real-time and big data applications of support vector algorithms are growing. Consequently, the Compute Unified Device Architecture (CUDA) parallelizing the procedure of support vector algorithms based on orthogonal kernel functions is presented. The book sheds light on how to use support vector algorithms based on orthogonal kernel functions in different situations and gives a significant perspective to all machine learning and scientific machine learning researchers all around the world to utilize fractional orthogonal kernel functions in their pattern recognition or scientific computing problems.
- Softcover
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EUR 160,49
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Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - The support vector machine (SVM) has become one of the standard tools for machine learning and data mining. This carefully edited volume presents the state of the art of the mathematical foundation of SVM in statistical learning theory, as well as no…vel algorithms and applications. Support Vector Machines provides a selection of numerous real-world applications, such as bioinformatics, text categorization, pattern recognition, and object detection, written by leading experts in their respective fields.
- Hardcover
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Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - The support vector machine (SVM) has become one of the standard tools for machine learning and data mining. This carefully edited volume presents the state of the art of the mathematical foundation of SVM in statistical learning theory, as well as novel alg…orithms and applications. Support Vector Machines provides a selection of numerous real-world applications, such as bioinformatics, text categorization, pattern recognition, and object detection, written by leading experts in their respective fields.
Learning With Fractional Orthogonal Kernel Classifiers in Support Vector Machines: Theory, Algorithms and Applications
Rad, Jamal Amani (Editor)/ Parand, Kourosh (Editor)/ Chakraverty, Snehashish (Editor)
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Hardcover. Zustand: Brand New. 319 pages. 9.25x6.10x9.21 inches. In Stock.
Support Vector Machines: Optimization Based Theory, Algorithms, and Extensions
Deng, Naiyang (Author)/ Tian, Yingjie (Author)/ Zhang, Chunhua (Author)
Sprache: Englisch
Verlag: Chapman & Hall, 2012
Serie: Buch 28 von 56 - Chapman & Hall/CRC Data Mining and Knowledge Discovery
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Hardcover. Zustand: Brand New. 1st edition. 300 pages. 9.45x6.46x0.98 inches. In Stock.
- Softcover
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Softcover. Zustand: gut. 2024. Learning with Fractional Orthogonal Kernel Classifiers in Support Vector Machines: Theory, Algorithms and Applications (Industrial and Applied Mathematics) In deutscher Sprache. pages.
- Softcover
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Paperback. Zustand: Brand New. 431 pages. 9.25x6.10x1.05 inches. In Stock.
