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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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Book ChapterDOI
A Random Sampling Technique for Training Support Vector Machines
TL;DR: This research is aiming to design efficient and theoretically guaranteed support vector machine training algorithms, and to develop systematic and efficient methods for finding "outliers", i.e., examples having an inherent error.
Journal ArticleDOI
Urban Image Classification With Semisupervised Multiscale Cluster Kernels
Devis Tuia,Gustau Camps-Valls +1 more
TL;DR: A semisupervised support vector machine (SVM) that integrates the information of both labeled and unlabeled pixels efficiently and is versatile and workable by unexperienced users is presented.
Journal ArticleDOI
Classifying protein sequences using hydropathy blocks
TL;DR: A new method for classifying protein sequences based upon the hydropathy blocks occurring in protein sequences by utilizing the support vector machine (SVM) classifier to classify the protein sequences into the known protein families.
Journal ArticleDOI
Primal twin support vector regression and its sparse approximation
TL;DR: A primal version for TSVR, termed primal TSVR (PTSVR), is first presented, which directly optimizes the pair of quadratic programming problems of TSVR in the primal space based on a series of sets of linear equations.
Journal ArticleDOI
Mathematics of the Neural Response
TL;DR: A recursive definition of the neural response and associated derived kernel is given that can be used in a variety of application domains such as classification of images, strings of text and genomics data.