Open AccessBook
An Introduction to Support Vector Machines
Reads0
Chats0
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
Citations
More filters
Journal ArticleDOI
Cell Death Discrimination with Raman Spectroscopy and Support Vector Machines
Georgios Pyrgiotakis,O. Erhun Kundakcioglu,Kathryn Ann Finton,Kathryn Ann Finton,Panos M. Pardalos,Kevin W. Powers,Brij M. Moudgil +6 more
TL;DR: The power of spectral resolution of Raman is combined with one of the most widely used machine learning techniques to assess the potential toxicity of chemical substances.
Journal ArticleDOI
Sparse LSSVM in Primal Using Cholesky Factorization for Large-Scale Problems
TL;DR: Experimental results on some large-scale nonlinear training problems show that the algorithms, based on P-LSSVM, can converge to acceptable test accuracies at very sparse solutions with a sparsity level <;1%, and even as little as 0.01%.
Book ChapterDOI
A fast dual method for HIK SVM learning
TL;DR: ICD is proposed, a deterministic and highly scalable dual space HIK SVM solver that achieves high accuracies using its default parameters in many datasets and empirically shows that ICD is not sensitive to the C parameter in SVM.
Journal ArticleDOI
Advances in predictive models for data mining
Se June Hong,Sholom M. Weiss +1 more
TL;DR: The key theoretical developments in PAC and statistical learning theory that have lead to the development of support vector machines and to the use of multiple models for increased predictive accuracy are reviewed.
Journal ArticleDOI
Empirical risk minimization for support vector classifiers
TL;DR: A general technique for solving support vector classifiers (SVCs) for an arbitrary loss function, relying on the application of an iterative reweighted least squares (IRWLS) procedure is proposed.