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An Introduction to Support Vector Machines
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This book is the first comprehensive introduction to Support Vector Machines, a new generation learning system based on recent advances in statistical learning theory, and introduces Bayesian analysis of learning and relates SVMs to Gaussian Processes and other kernel based learning methods.Abstract:
This book is the first comprehensive introduction to Support Vector Machines (SVMs), a new generation learning system based on recent advances in statistical learning theory. The book also introduces Bayesian analysis of learning and relates SVMs to Gaussian Processes and other kernel based learning methods. SVMs deliver state-of-the-art performance in real-world applications such as text categorisation, hand-written character recognition, image classification, biosequences analysis, etc. Their first introduction in the early 1990s lead to a recent explosion of applications and deepening theoretical analysis, that has now established Support Vector Machines along with neural networks 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 application of these techniques. The concepts are introduced gradually in accessible and self-contained stages, though in each stage the presentation is rigorous and thorough. Pointers to relevant literature and web sites containing software ensure that it forms an ideal starting point for further study. Equally the book will equip the practitioner to apply the techniques and an associated web site will provide pointers to updated literature, new applications, and on-line software.read more
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Milos: A Multimedia Content Management System for Digital Library Applications
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Learning Probabilistic Linear-Threshold Classifiers via Selective Sampling
TL;DR: This paper investigates selective sampling, a learning model where the learner observes a sequence of i.i.d. unlabeled instances each time deciding whether to query the label of the current instance, and introduces a new selective sampling rule that can learn nonlinear probabilistic functions via the kernel machinery.
Journal ArticleDOI
Advanced support vector machines for 802.11 indoor location
Carlos Figuera,José Luis Rojo-Álvarez,Mark Richard Wilby,Inmaculada Mora-Jiménez,Antonio J. Caamaño +4 more
TL;DR: A technique to enhance algorithms based on Wi-Fi measurements of the received signal strength by including certain a priori information within the learning machine, using the spectral information of the training set, and a complex output to take advantage of the cross information in the two dimensions of the location.