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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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Proceedings Article
Text Classification using String Kernels
TL;DR: In this article, an inner product in the feature space consisting of all subsequences of length k was introduced for comparing two text documents, where a subsequence is any ordered sequence of k characters occurring in the text though not necessarily contiguously.
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Kernel-based methods for hyperspectral image classification
TL;DR: This paper assesses performance of regularized radial basis function neural networks (Reg-RBFNN), standard support vector machines (SVMs), kernel Fisher discriminant (KFD) analysis, and regularized AdaBoost (reg-AB) in the context of hyperspectral image classification.
Journal ArticleDOI
Natural language processing: an introduction.
TL;DR: The historical evolution of NLP is described, and common NLP sub-problems in this extensive field are summarized, and possible future directions for NLP are considered.
Journal ArticleDOI
A GA-based feature selection and parameters optimizationfor support vector machines
Cheng-Lung Huang,Chieh-Jen Wang +1 more
TL;DR: This research presents a genetic algorithm approach for feature selection and parameters optimization to solve the problem of optimizing parameters and feature subset without degrading the SVM classification accuracy.
Proceedings Article
A kernel method for multi-labelled classification
André Elisseeff,Jason Weston +1 more
TL;DR: This article presents a Support Vector Machine like learning system to handle multi-label problems, based on a large margin ranking system that shares a lot of common properties with SVMs.