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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
Learning Categories From Few Examples With Multi Model Knowledge Transfer
TL;DR: This paper presents a discriminative model adaptation algorithm able to proficiently learn a target object with few examples by relying on other previously learned source categories by solving a convex optimization problem.
Journal ArticleDOI
Data classification with radial basis function networks based on a novel kernel density estimation algorithm
TL;DR: This work presents a novel learning algorithm for efficient construction of the radial basis function (RBF) networks that can deliver the same level of accuracy as the support vector machines (SVMs) in data classification applications and compares the performance of the RBF networks constructed with the proposed learning algorithm and those constructed with a conventional cluster-based learning algorithm.
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Support vector machine applied to settlement of shallow foundations on cohesionless soils
TL;DR: In this article, a support vector machine (SVM) was used to predict the settlement of shallow foundations on cohesionless soil, and a thorough sensitive analysis has been made to ascertain which parameters are having maximum influence on settlement.
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Rainfall and runoff forecasting with SSA-SVM approach
TL;DR: In this paper, a simple and efficient prediction technique based on Singular Spectrum Analysis (SSA) coupled with Support Vector Machine (SVM) is proposed to predict the Tryggevaelde catchment runoff data (Denmark) and the Singapore rainfall data as case studies.
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On the eigenspectrum of the gram matrix and the generalization error of kernel-PCA
TL;DR: The differences between the two spectra are bounded and a performance bound on kernel principal component analysis (PCA) is provided showing that good performance can be expected even in very-high-dimensional feature spaces provided the sample eigenvalues fall sufficiently quickly.