Learning with Support Vector Machines von Colin Campbell; Yiming Ying: Good Soft Cover (2011)

Beschreibung

Ex-library with the usual features. Library label on front cover. The interior is clean and tight. Binding is good. Cover shows light wear.

Bestandsnummer des Verk�ufers 122152x

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Bibliografische Details

Titel: Learning with Support Vector Machines
Verlag: Morgan & Claypool
Ver�ffentlichungsdatum: 2011
Sprache: Englisch
ISBN-10: 1608456161
ISBN-13: 9781608456161
Bindung: Soft Cover
Zustand: Good
Buchtyp: Ex-Library

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Inhaltsangabe

Support Vectors Machines have become a well established tool within machine learning. They work well in practice and have now been used across a wide range of applications from recognizing hand-written digits, to face identification, text categorisation, bioinformatics, and database marketing. In this book we give an introductory overview of this subject. We start with a simple Support Vector Machine for performing binary classification before considering multi-class classification and learning in the presence of noise. We show that this framework can be extended to many other scenarios such as prediction with real-valued outputs, novelty detection and the handling of complex output structures such as parse trees. Finally, we give an overview of the main types of kernels which are used in practice and how to learn and make predictions from multiple types of input data. Table of Contents: Support Vector Machines for Classification / Kernel-based Models / Learning with Kernels

Rese�a del editor

Support Vectors Machines have become a well established tool within machine learning. They work well in practice and have now been used across a wide range of applications from recognizing hand-written digits, to face identification, text categorisation, bioinformatics, and database marketing. In this book we give an introductory overview of this subject. We start with a simple Support Vector Machine for performing binary classification before considering multi-class classification and learning in the presence of noise. We show that this framework can be extended to many other scenarios such as prediction with real-valued outputs, novelty detection and the handling of complex output structures such as parse trees. Finally, we give an overview of the main types of kernels which are used in practice and how to learn and make predictions from multiple types of input data. Table of Contents: Support Vector Machines for Classification / Kernel-based Models / Learning with Kernels

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