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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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Journal ArticleDOI
Multisurface proximal support vector machine classification via generalized eigenvalues
Olvi L. Mangasarian,E.W. Wild +1 more
TL;DR: Tests on simple examples as well as on a number of public data sets show the advantages of the proposed approach in both computation time and test set correctness.
Journal ArticleDOI
Adversarial Machine Learning
TL;DR: The author briefly introduces the emerging field of adversarial machine learning, in which opponents can cause traditional machine learning algorithms to behave poorly in security applications.
Journal Article
Variable selection using svm based criteria
TL;DR: New methods to evaluate variable subset relevance with a view to variable selection based on weight vector derivative achieves good results and performs consistently well over the datasets used.
Book ChapterDOI
Machine Learning for Sequential Data: A Review
TL;DR: This paper formalizes the principal learning tasks and describes the methods that have been developed within the machine learning research community for addressing these problems, including sliding window methods, recurrent sliding windows, hidden Markov models, conditional random fields, and graph transformer networks.
Journal ArticleDOI
Benchmarking Least Squares Support Vector Machine Classifiers
Tony Van Gestel,Johan A. K. Suykens,Bart Baesens,Stijn Viaene,Jan Vanthienen,Guido Dedene,Bart De Moor,Joos Vandewalle +7 more
TL;DR: Both the SVM and LS-SVM classifier with RBF kernel in combination with standard cross-validation procedures for hyperparameter selection achieve comparable test set performances, consistently very good when compared to a variety of methods described in the literature.