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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.read more
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Journal ArticleDOI
Fixed-size least squares support vector machines: a large scale application in electrical load forecasting
TL;DR: Based on the Nyström approximation and the primal-dual formulation of the least squares support vector machines, it becomes possible to apply a nonlinear model to a large scale regression problem by using a sparse approximation of the nonlinear mapping induced by the kernel matrix.
Book ChapterDOI
Mining biosignal data: coronary artery disease diagnosis using linear and nonlinear features of HRV
TL;DR: A novel methodology to develop the multi-parametric feature including linear and nonlinear features of HRV (Heart Rate Variability) diagnosing cardiovascular disease is proposed and SVM outperformed the other classifiers.
Journal ArticleDOI
Linear dependency between /spl epsi/ and the input noise in /spl epsi/-support vector regression
James T. Kwok,Ivor W. Tsang +1 more
TL;DR: The resultant predicted choice of /spl epsi/ is much closer to the experimentally observed optimal value, while again demonstrating a linear trend with the input noise.
Journal ArticleDOI
Handling missing values in support vector machine classifiers
TL;DR: This paper discusses the task of learning a classifier from observed data containing missing values amongst the inputs which are missing completely at random and a non-parametric perspective is adopted by defining a modified risk taking into account the uncertainty of the predicted outputs when missing values are involved.
Journal ArticleDOI
A Review of Indirect Tool Condition Monitoring Systems and Decision-Making Methods in Turning: Critical Analysis and Trends.
Mustafa Kuntoğlu,Abdullah Aslan,Danil Yurievich Pimenov,Üsame Ali Usca,Emin Salur,Munish Kumar Gupta,Munish Kumar Gupta,Tadeusz Mikolajczyk,Khaled Giasin,Wojciech Kapłonek,Shubham Sharma +10 more
TL;DR: In this paper, the effect of sensorial data on tool wear by considering previous published papers is discussed, and the main aim is to discuss the impact of sensual data on tools' wear and surface roughness.