EAN: 9783031004247

SYNTHESIS LECTURES ON ARTIFICIAL INTELLIGENCE A...

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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