Introduction to support vector machines and other kernel-based learning methods

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Langue : Anglais
Couverture de l'ouvrage Introduction to support vector machines and other kernel-based learning methods

Thèmes d'Introduction to support vector machines and other...

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Ouvrage 188 p. · 18x25.3 cm · Relié
This is the first comprehensive introduction to Support Vector Machines (SVMs), a new generation learning system based on recent advances in statistical learning theory. SVMs deliver state-of-the-art performance in real-world applications such as text categorisation, hand-written character recognition, image classification, biosequences analysis, etc., and are now established as one of the standard tools for machine learning and data mining. 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 learning methodology. Linear learning machines. Kernel-induced feature spaces. Generalization theory. Optimization theory. Support vector machines. Implementation techniques. Applications of support vector machines.