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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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Large-Scale Support Vector Machines: Algorithms and Theory
TL;DR: This document surveys work on SVM training methods that target this large-scale learning regime, and discusses why SGD generalizes well even though it is poor at optimization, and describes algorithms such as Pegasos and FOLOS that extend basic SGD to quickly solve the SVM problem.
Journal ArticleDOI
Classifying NIR spectra of textile products with kernel methods
TL;DR: Results show that these wavelengths seen on the NIR spectra are not sufficient to build a machine with correct generalisation ability, so the use of a non-linear method, such as SVM and its corollary methods, kernel alignment and k-PCA is justified.
Proceedings ArticleDOI
Performance analysis for computer-aided lung nodule detection on LIDC data
Roland Opfer,Rafael Wiemker +1 more
TL;DR: The first step to use the LIDC data as a benchmark test for computer aided detection for pulmonary nodules shows a detection rate of 89 % at a median false positive rate of 2 findings per patient.
Proceedings ArticleDOI
Extracting key-substring-group features for text classification
Dell Zhang,Wee Sun Lee +1 more
TL;DR: This paper proposes to partition all substrings into statistical equivalence groups, and then pick those groups which are important (in the statistical sense) as features (named key-substring-group features) for text classification, and proposes a suffix tree based algorithm that can extract such features in linear time.
Journal ArticleDOI
Margin-Maximizing Feature Elimination Methods for Linear and Nonlinear Kernel-Based Discriminant Functions
TL;DR: This work proves that RFE is not consistent with margin maximization, central to the SVM learning approach, and proposes explicit margin-based feature elimination (MFE) for SVMs and demonstrates both improved margin and improved generalization, compared with RFE.