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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
Citations
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Journal ArticleDOI
Efficient hyperkernel learning using second-order cone programming
TL;DR: This paper shows that this kernel function learning problem can be equivalently reformulated as a second-order cone program (SOCP), which can then be solved more efficiently than SDPs.
Journal ArticleDOI
Predicting WWW surfing using multiple evidence combination
TL;DR: This paper combines two classification techniques, namely, the Markov model and Support Vector Machines (SVM), to resolve prediction using Dempster’s rule, and applies feature extraction to increase the power of discrimination of SVM.
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Analysis of the Distance Between Two Classes for Tuning SVM Hyperparameters
TL;DR: This paper proposes a novel method for tuning the hyperparameters by maximizing the distance between two classes (DBTC) in the feature space by developing a gradient-based algorithm to search the optimal kernel parameter.
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Asset portfolio optimization using support vector machines and real-coded genetic algorithm
TL;DR: Support Vector Machines are used for classifying financial assets in three pre-defined classes, based on their performance on some key financial criteria, and Real-Coded Genetic Algorithm is employed to solve the mathematical model of the multicriteria portfolio selection problem in the respective classes incorporating investor-preferences.
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Relating HIV-1 sequence variation to replication capacity via trees and forests.
TL;DR: In this paper, the problem of relating genotype (as represented by amino acid sequence) to phenotypes is distinguished from standard regression problems by the nature of sequence data, and a variety of data analytic methods have been proposed in this context.