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An Introduction to Support Vector Machines
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TLDR
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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Journal ArticleDOI
Robust Truncated Hinge Loss Support Vector Machines
Yichao Wu,Yufeng Liu +1 more
TL;DR: The robust truncated hinge loss SVM (RSVM) is proposed, which is shown to be more robust to outliers and to deliver more accurate classifiers using a smaller set of SVs than the standard SVM.
Journal ArticleDOI
Fault diagnosis of ball bearings using machine learning methods
TL;DR: The results show that the machine learning algorithms can be used for automated diagnosis of bearing faults and it is observed that the severe (chaotic) vibrations occur under bearings with rough inner race surface and ball with corrosion pitting.
Journal ArticleDOI
Combining pairwise sequence similarity and support vector machines for detecting remote protein evolutionary and structural relationships.
Li Liao,William Stafford Noble +1 more
TL;DR: A means of representing proteins using pairwise sequence similarity scores, combined with a discriminative classification algorithm known as the support vector machine (SVM), provides a powerful means of detecting subtle structural and evolutionary relationships among proteins.
Online Learning: Theory, Algorithms, and Applications
TL;DR: This dissertation describes a novel framework for the design and analysis of online learning algorithms and proposes a new perspective on regret bounds which is based on the notion of duality in convex optimization.
Proceedings ArticleDOI
Feature selection for support vector machines by means of genetic algorithm
TL;DR: This paper presents a special genetic algorithm, which especially takes into account the existing bounds on the generalization error for support vector machines (SVMs), which is compared to the traditional method of performing cross-validation and to other existing algorithms for feature selection.