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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
Support vector regression for porosity prediction in a heterogeneous reservoir: A comparative study
A. F. Al-Anazi,Ian D. Gates +1 more
TL;DR: Support vector machines are explored as an intelligent technique to correlate porosity to well log data and the results reveal that the SVR method exhibits superior accuracy and robustness with respect to these neural network methods especially withrespect to accuracy when generalizing to previously unseen porosity data.
Journal ArticleDOI
Bearing fault prognosis based on health state probability estimation
TL;DR: In this article, a technique for accurate assessment of the remnant life of bearings based on health state probability estimation and historical knowledge embedded in the closed loop diagnostics and prognostics system is described.
Proceedings ArticleDOI
Training Support Vector Machine using Adaptive Clustering
Daniel Boley,Dongwei Cao +1 more
TL;DR: An algorithm called ClusterSVM is proposed that accelerates the training process by exploiting the distributional properties of the training data, that is, the natural clustering of theTraining data and the overall layout of these clusters relative to the decision boundary of support vector machines.
Journal Article
Worst-Case Analysis of Selective Sampling for Linear Classification
TL;DR: This paper introduces a general technique for turning linear-threshold classification algorithms from the general additive family into randomized selective sampling algorithms, and shows that these semi-supervised algorithms can achieve, on average, the same accuracy as that of their fully supervised counterparts, but using fewer labels.
Journal ArticleDOI
Classification of a large microarray data set: Algorithm comparison and analysis of drug signatures
Georges Natsoulis,Laurent El Ghaoui,Gert R. G. Lanckriet,Alexander M. Tolley,Fabrice Leroy,Shane Dunlea,Barrett P. Eynon,Cecelia Pearson,Stuart Tugendreich,Kurt Jarnagin +9 more
TL;DR: These studies show that several types of linear classifiers based on Support Vector Machines (SVMs) and Logistic Regression can be used to derive readily interpretable drug signatures with high classification performance.