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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.

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Proceedings ArticleDOI

Stochastic methods for l1 regularized loss minimization

TL;DR: In this article, the authors describe and analyze two stochastic methods for l 1 regularized loss minimization problems, such as the Lasso, where the choice of feature/example is uniformly at random.
Journal Article

Distributional word clusters vs. words for text categorization

TL;DR: An approach to text categorization that combines distributional clustering of words and a Support Vector Machine (SVM) classifier with a word-cluster representation is studied, which significantly outperforms the word-based representation in terms of categorization accuracy or representation efficiency.
Proceedings ArticleDOI

How does clickthrough data reflect retrieval quality

TL;DR: A sequence of studies investigating the relationship between observable user behavior and retrieval quality for an operational search engine on the arXiv.org e-print archive finds that paired experiment designs adapted from sensory analysis produce accurate and reliable statements about the relative quality of two retrieval functions.
Journal ArticleDOI

Impact of imputation of missing values on classification error for discrete data

TL;DR: It is shown that imputation with the tested methods on average improves classification accuracy when compared to classification without imputation, and some classifiers such as C4.5 and Nai@?ve-Bayes were found to be missing data resistant, i.e., they can produce accurate classification in the presence of missing data.
Journal ArticleDOI

Overview of Advanced Computer Vision Systems for Skin Lesions Characterization

TL;DR: This paper presents the installation, the visual features used for skin lesion classification, and the methods for defining them, and describes how to extract these features through digital image processing methods, i.e., segmentation, border detection, and color and texture processing.