Kernel Methods and Machine Learning
Kernel Methods and Machine Learning

Author: S. Y. Kung

Publisher: Cambridge University Press

Published: 2014-04-17

Total Pages: 617

ISBN-13: 1139867636

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Offering a fundamental basis in kernel-based learning theory, this book covers both statistical and algebraic principles. It provides over 30 major theorems for kernel-based supervised and unsupervised learning models. The first of the theorems establishes a condition, arguably necessary and sufficient, for the kernelization of learning models. In addition, several other theorems are devoted to proving mathematical equivalence between seemingly unrelated models. With over 25 closed-form and iterative algorithms, the book provides a step-by-step guide to algorithmic procedures and analysing which factors to consider in tackling a given problem, enabling readers to improve specifically designed learning algorithms, build models for new applications and develop efficient techniques suitable for green machine learning technologies. Numerous real-world examples and over 200 problems, several of which are Matlab-based simulation exercises, make this an essential resource for graduate students and professionals in computer science, electrical and biomedical engineering. Solutions to problems are provided online for instructors.

Learning with Kernels
Learning with Kernels

Author: Bernhard Scholkopf

Publisher: MIT Press

Published: 2018-06-05

Total Pages: 645

ISBN-13: 0262536579

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A comprehensive introduction to Support Vector Machines and related kernel methods. In the 1990s, a new type of learning algorithm was developed, based on results from statistical learning theory: the Support Vector Machine (SVM). This gave rise to a new class of theoretically elegant learning machines that use a central concept of SVMs—-kernels—for a number of learning tasks. Kernel machines provide a modular framework that can be adapted to different tasks and domains by the choice of the kernel function and the base algorithm. They are replacing neural networks in a variety of fields, including engineering, information retrieval, and bioinformatics. Learning with Kernels provides an introduction to SVMs and related kernel methods. Although the book begins with the basics, it also includes the latest research. It provides all of the concepts necessary to enable a reader equipped with some basic mathematical knowledge to enter the world of machine learning using theoretically well-founded yet easy-to-use kernel algorithms and to understand and apply the powerful algorithms that have been developed over the last few years.

Kernel Methods in Computer Vision
Kernel Methods in Computer Vision

Author: Christoph H. Lampert

Publisher: Now Publishers Inc

Published: 2009

Total Pages: 113

ISBN-13: 1601982682

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Few developments have influenced the field of computer vision in the last decade more than the introduction of statistical machine learning techniques. Particularly kernel-based classifiers, such as the support vector machine, have become indispensable tools, providing a unified framework for solving a wide range of image-related prediction tasks, including face recognition, object detection and action classification. By emphasizing the geometric intuition that all kernel methods rely on, Kernel Methods in Computer Vision provides an introduction to kernel-based machine learning techniques accessible to a wide audience including students, researchers and practitioners alike, without sacrificing mathematical correctness. It covers not only support vector machines but also less known techniques for kernel-based regression, outlier detection, clustering and dimensionality reduction. Additionally, it offers an outlook on recent developments in kernel methods that have not yet made it into the regular textbooks: structured prediction, dependency estimation and learning of the kernel function. Each topic is illustrated with examples of successful application in the computer vision literature, making Kernel Methods in Computer Vision a useful guide not only for those wanting to understand the working principles of kernel methods, but also for anyone wanting to apply them to real-life problems.

Machine Learning with SVM and Other Kernel Methods
Machine Learning with SVM and Other Kernel Methods

Author: K.P. Soman

Publisher: PHI Learning Pvt. Ltd.

Published: 2009-02-02

Total Pages: 495

ISBN-13: 8120334353

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Support vector machines (SVMs) represent a breakthrough in the theory of learning systems. It is a new generation of learning algorithms based on recent advances in statistical learning theory. Designed for the undergraduate students of computer science and engineering, this book provides a comprehensive introduction to the state-of-the-art algorithm and techniques in this field. It covers most of the well known algorithms supplemented with code and data. One Class, Multiclass and hierarchical SVMs are included which will help the students to solve any pattern classification problems with ease and that too in Excel. KEY FEATURES  Extensive coverage of Lagrangian duality and iterative methods for optimization  Separate chapters on kernel based spectral clustering, text mining, and other applications in computational linguistics and speech processing  A chapter on latest sequential minimization algorithms and its modifications to do online learning  Step-by-step method of solving the SVM based classification problem in Excel.  Kernel versions of PCA, CCA and ICA The CD accompanying the book includes animations on solving SVM training problem in Microsoft EXCEL and by using SVMLight software . In addition, Matlab codes are given for all the formulations of SVM along with the data sets mentioned in the exercise section of each chapter.

Kernel Methods for Remote Sensing Data Analysis
Kernel Methods for Remote Sensing Data Analysis

Author: Gustau Camps-Valls

Publisher: John Wiley & Sons

Published: 2009-09-03

Total Pages: 434

ISBN-13: 0470749008

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Kernel methods have long been established as effective techniques in the framework of machine learning and pattern recognition, and have now become the standard approach to many remote sensing applications. With algorithms that combine statistics and geometry, kernel methods have proven successful across many different domains related to the analysis of images of the Earth acquired from airborne and satellite sensors, including natural resource control, detection and monitoring of anthropic infrastructures (e.g. urban areas), agriculture inventorying, disaster prevention and damage assessment, and anomaly and target detection. Presenting the theoretical foundations of kernel methods (KMs) relevant to the remote sensing domain, this book serves as a practical guide to the design and implementation of these methods. Five distinct parts present state-of-the-art research related to remote sensing based on the recent advances in kernel methods, analysing the related methodological and practical challenges: Part I introduces the key concepts of machine learning for remote sensing, and the theoretical and practical foundations of kernel methods. Part II explores supervised image classification including Super Vector Machines (SVMs), kernel discriminant analysis, multi-temporal image classification, target detection with kernels, and Support Vector Data Description (SVDD) algorithms for anomaly detection. Part III looks at semi-supervised classification with transductive SVM approaches for hyperspectral image classification and kernel mean data classification. Part IV examines regression and model inversion, including the concept of a kernel unmixing algorithm for hyperspectral imagery, the theory and methods for quantitative remote sensing inverse problems with kernel-based equations, kernel-based BRDF (Bidirectional Reflectance Distribution Function), and temperature retrieval KMs. Part V deals with kernel-based feature extraction and provides a review of the principles of several multivariate analysis methods and their kernel extensions. This book is aimed at engineers, scientists and researchers involved in remote sensing data processing, and also those working within machine learning and pattern recognition.

Digital Signal Processing with Kernel Methods
Digital Signal Processing with Kernel Methods

Author: Jose Luis Rojo-Alvarez

Publisher: John Wiley & Sons

Published: 2018-02-05

Total Pages: 665

ISBN-13: 1118611799

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A realistic and comprehensive review of joint approaches to machine learning and signal processing algorithms, with application to communications, multimedia, and biomedical engineering systems Digital Signal Processing with Kernel Methods reviews the milestones in the mixing of classical digital signal processing models and advanced kernel machines statistical learning tools. It explains the fundamental concepts from both fields of machine learning and signal processing so that readers can quickly get up to speed in order to begin developing the concepts and application software in their own research. Digital Signal Processing with Kernel Methods provides a comprehensive overview of kernel methods in signal processing, without restriction to any application field. It also offers example applications and detailed benchmarking experiments with real and synthetic datasets throughout. Readers can find further worked examples with Matlab source code on a website developed by the authors: http://github.com/DSPKM • Presents the necessary basic ideas from both digital signal processing and machine learning concepts • Reviews the state-of-the-art in SVM algorithms for classification and detection problems in the context of signal processing • Surveys advances in kernel signal processing beyond SVM algorithms to present other highly relevant kernel methods for digital signal processing An excellent book for signal processing researchers and practitioners, Digital Signal Processing with Kernel Methods will also appeal to those involved in machine learning and pattern recognition.

Kernel-based Data Fusion for Machine Learning
Kernel-based Data Fusion for Machine Learning

Author: Shi Yu

Publisher: Springer

Published: 2011-03-29

Total Pages: 223

ISBN-13: 3642194060

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Data fusion problems arise frequently in many different fields. This book provides a specific introduction to data fusion problems using support vector machines. In the first part, this book begins with a brief survey of additive models and Rayleigh quotient objectives in machine learning, and then introduces kernel fusion as the additive expansion of support vector machines in the dual problem. The second part presents several novel kernel fusion algorithms and some real applications in supervised and unsupervised learning. The last part of the book substantiates the value of the proposed theories and algorithms in MerKator, an open software to identify disease relevant genes based on the integration of heterogeneous genomic data sources in multiple species. The topics presented in this book are meant for researchers or students who use support vector machines. Several topics addressed in the book may also be interesting to computational biologists who want to tackle data fusion challenges in real applications. The background required of the reader is a good knowledge of data mining, machine learning and linear algebra.