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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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Proceedings ArticleDOI
Efficient and effective grasping of novel objects through learning and adapting a knowledge base
N. Curtis,Jing Xiao +1 more
TL;DR: This paper introduces a new approach to establish a good grasp for a novel object quickly that takes into account the geometrical and physical knowledge of grasping and shows the effectiveness of this approach to achieve quick and good grasps of novel objects.
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Hand-Geometry Recognition Using Entropy-Based Discretization
Ajay Kumar,David Zhang +1 more
TL;DR: This paper proposes employing discretization of hand-geometry features, using entropy-based heuristics, to achieve the performance improvement, and achieves significant improvement in the recognition accuracy.
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Land-Use-Change Modeling Using Unbalanced Support-Vector Machines
TL;DR: This paper presents a novel method to model urban land-use change using support-vector machines (SVMs), a new generation of machine learning algorithms used in classification and regression domains, and demonstrates that the unbalanced SVMs can achieve high and reliable performance for land- use-change modeling.
Journal ArticleDOI
Kernel Machine SNP-set Analysis for Censored Survival Outcomes in Genome-wide Association Studies
TL;DR: A powerful test for identifying single nucleotide polymorphism (SNP)‐sets that are predictive of survival with data from genome‐wide association studies with censored survival outcomes is developed.