Support vector machines applications (77 résultats)

hardcover. Etat : As New. Like New Condition.Crisp pages. Clean cover and pages. Book shows no shelf wear. Not Satisfied? Contact us to get a refund.

Etat : New.

Etat : New.

Etat : As New. Unread book in perfect condition.

Etat : New.

Etat : As New. Unread book in perfect condition.

Etat : Like New. Text block firm and clean, binding unblemished, boards straight, without highlights or underlining. Without any discs, access codes or extra items. Supporting Bay Area Friends of the Library since 2010. Well packaged and promptly shipped.

hardcover. Etat : Sehr gut. 225 Seiten; 9783319461847.2 Gewicht in Gramm: 1.

hardcover. Etat : new.

Taschenbuch. Etat : Neu. Support Vector Machines and Particle Swarm Optimization | Applications to Reliability Prediction | Isis Didier Lins (u. a.) | Taschenbuch | 92 S. | Englisch | 2010 | LAP LAMBERT Academic Publishing | EAN 9783838319407 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. …

Taschenbuch. Etat : Neu. Human Face Recognition with Support Vector Machines | Algorithms and Applications | Latha Parthiban | Taschenbuch | Einband - flex.(Paperback) | Englisch | 2011 | VDM Verlag Dr. Müller | EAN 9783639366990 | Verantwortliche Person für die EU: OmniScriptum GmbH & Co. KG, Bahnhofstr. 28, 66111 Saarbrücken, info[at]akademikerverlag[dot]de | Anbieter: preigu. …

Etat : As New. Unread book in perfect condition.

Etat : New.

Etat : Sehr gut. Zustand: Sehr gut | Sprache: Englisch | Produktart: Bücher | This book provides a systematic and focused study of the various aspects of twin support vector machines (TWSVM) and related developments for classification and regression. In addition to presenting most of the basic models of TWSVM and twin support vector regression (TWSVR) available in the literature, it also discusses the important and challenging applications of this new machine learning methodology. A chapter on ¿Additional Topics¿ has been included to discuss kernel optimization and support tensor machine topics, which are comparatively new but have great potential in applications. It is primarily written for graduate students and researchers in the area of machine learning and related topics in computer science, mathematics, electrical engineering, management science and finance.…

Etat : New.

Etat : New. pp. 211.

Taschenbuch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - This book provides a systematic and focused study of the various aspects of twin support vector machines (TWSVM) and related developments for classification and regression. In addition to presenting most of the basic models of TWSVM and twin support vector regression (TWSVR) available in the literature, it also discusses the important and challenging applications of this new machine learning methodology. A chapter on 'Additional Topics' has been included to discuss kernel optimization and support tensor machine topics, which are comparatively new but have great potential in applications. It is primarily written for graduate students and researchers in the area of machine learning and related topics in computer science, mathematics, electrical engineering, management science and finance.…

Buch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - This book provides a systematic and focused study of the various aspects of twin support vector machines (TWSVM) and related developments for classification and regression. In addition to presenting most of the basic models of TWSVM and twin support vector regression (TWSVR) available in the literature, it also discusses the important and challenging applications of this new machine learning methodology. A chapter on 'Additional Topics' has been included to discuss kernel optimization and support tensor machine topics, which are comparatively new but have great potential in applications. It is primarily written for graduate students and researchers in the area of machine learning and related topics in computer science, mathematics, electrical engineering, management science and finance.…

Etat : Brand New. New. US edition. Expediting shipping for all USA and Europe orders excluding PO Box. Excellent Customer Service.

Etat : New. In English.

Taschenbuch. Etat : Neu. Twin Support Vector Machines | Models, Extensions and Applications | Jayadeva (u. a.) | Taschenbuch | xiv | Englisch | 2018 | Springer | EAN 9783319834627 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu. …

Paperback. Etat : Like New. LIKE NEW. SHIPS FROM MULTIPLE LOCATIONS. book.

Etat : New.

Etat : As New. Unread book in perfect condition.

Etat : Sehr gut. Zustand: Sehr gut | Seiten: 312 | Sprache: Englisch | Produktart: Bücher | Support vector machines (SVM) have both a solid mathematical background and practical applications. This book focuses on the recent advances and applications of the SVM, such as image processing, medical practice, computer vision, and pattern recognition, machine learning, applied statistics, and artificial intelligence. The aim of this book is to create a comprehensive source on support vector machine applications.…

Etat : 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.…

Taschenbuch. Etat : 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 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu. …

Etat : New.

Taschenbuch. Etat : 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.…

Buch. Etat : 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.…