<span>This is the first comprehensive introduction to Support Vector Machines (SVMs), a new generation learning system based on recent advances in statistical learning theory. Students will find the book both stimulating and accessible, while practitioners will be guided smoothly through the materia
An Introduction to Support Vector Machines and Other Kernel-based Learning Methods
β Scribed by Nello Cristianini, John Shawe-Taylor
- Publisher
- Cambridge University Press
- Year
- 2013
- Tongue
- English
- Leaves
- 204
- Category
- Library
No coin nor oath required. For personal study only.
β¦ Synopsis
This is the first comprehensive introduction to Support Vector Machines (SVMs), a new generation learning system based on recent advances in statistical learning theory. Students will find the book both stimulating and accessible, while practitioners will be guided smoothly through the material required for a good grasp of the theory and its applications. The concepts are introduced gradually in accessible and self-contained stages, while the presentation is rigorous and thorough. Pointers to relevant literature and web sites containing software make it an ideal starting point for further study.
β¦ Subjects
Computers & Technology;Business Technology;Certification;Computer Science;Databases & Big Data;Digital Audio, Video & Photography;Games & Strategy Guides;Graphics & Design;Hardware & DIY;History & Culture;Internet & Social Media;Mobile Phones, Tablets & E-Readers;Networking & Cloud Computing;Operating Systems;Programming;Programming Languages;Security & Encryption;Software;Web Development & Design;Bioinformatics;Biological Sciences;Science & Math;Computer Science;Algorithms;Artificial Intelligen
π SIMILAR VOLUMES
This is the first comprehensive introduction to SVMs, a new generation learning system based on recent advances in statistical learning theory; it will help readers understand the theory and its real-world applications.
This is the first comprehensive introduction to Support Vector Machines (SVMs), a new generation learning system based on recent advances in statistical learning theory. Students will find the book both stimulating and accessible, while practitioners will be guided smoothly through the material requ
<span>The Support Vector Machine is a powerful new learning algorithm for solving a variety of learning and function estimation problems, such as pattern recognition, regression estimation, and operator inversion. The impetus for this collection was a workshop on Support Vector Machines held at the