Kernel Methods And Machine Learning

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Kernel Methods and Machine Learning

Author : S. Y. Kung
Publisher : Cambridge University Press
ISBN 13 : 1139867636
Total Pages : 617 pages
Book Rating : 4.1/5 (398 download)

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Book Synopsis Kernel Methods and Machine Learning by : S. Y. Kung

Download or read book Kernel Methods and Machine Learning written by S. Y. Kung and published by Cambridge University Press. This book was released on 2014-04-17 with total page 617 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Author : Bernhard Scholkopf
Publisher : MIT Press
ISBN 13 : 0262536579
Total Pages : 645 pages
Book Rating : 4.2/5 (625 download)

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Book Synopsis Learning with Kernels by : Bernhard Scholkopf

Download or read book Learning with Kernels written by Bernhard Scholkopf and published by MIT Press. This book was released on 2018-06-05 with total page 645 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 for Pattern Analysis

Author : John Shawe-Taylor
Publisher : Cambridge University Press
ISBN 13 : 9780521813976
Total Pages : 520 pages
Book Rating : 4.8/5 (139 download)

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Book Synopsis Kernel Methods for Pattern Analysis by : John Shawe-Taylor

Download or read book Kernel Methods for Pattern Analysis written by John Shawe-Taylor and published by Cambridge University Press. This book was released on 2004-06-28 with total page 520 pages. Available in PDF, EPUB and Kindle. Book excerpt: Publisher Description

Kernel Methods in Computational Biology

Author : Bernhard Schölkopf
Publisher : MIT Press
ISBN 13 : 9780262195096
Total Pages : 428 pages
Book Rating : 4.1/5 (95 download)

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Book Synopsis Kernel Methods in Computational Biology by : Bernhard Schölkopf

Download or read book Kernel Methods in Computational Biology written by Bernhard Schölkopf and published by MIT Press. This book was released on 2004 with total page 428 pages. Available in PDF, EPUB and Kindle. Book excerpt: A detailed overview of current research in kernel methods and their application to computational biology.

Kernel Methods in Computer Vision

Author : Christoph H. Lampert
Publisher : Now Publishers Inc
ISBN 13 : 1601982682
Total Pages : 113 pages
Book Rating : 4.6/5 (19 download)

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Book Synopsis Kernel Methods in Computer Vision by : Christoph H. Lampert

Download or read book Kernel Methods in Computer Vision written by Christoph H. Lampert and published by Now Publishers Inc. This book was released on 2009 with total page 113 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Author : K.P. Soman
Publisher : PHI Learning Pvt. Ltd.
ISBN 13 : 8120334353
Total Pages : 495 pages
Book Rating : 4.1/5 (23 download)

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Book Synopsis Machine Learning with SVM and Other Kernel Methods by : K.P. Soman

Download or read book Machine Learning with SVM and Other Kernel Methods written by K.P. Soman and published by PHI Learning Pvt. Ltd.. This book was released on 2009-02-02 with total page 495 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Advances in Kernel Methods

Author : Bernhard Schölkopf
Publisher : MIT Press
ISBN 13 : 9780262194167
Total Pages : 400 pages
Book Rating : 4.1/5 (941 download)

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Book Synopsis Advances in Kernel Methods by : Bernhard Schölkopf

Download or read book Advances in Kernel Methods written by Bernhard Schölkopf and published by MIT Press. This book was released on 1999 with total page 400 pages. Available in PDF, EPUB and Kindle. Book excerpt: A young girl hears the story of her great-great-great-great- grandfather and his brother who came to the United States to make a better life for themselves helping to build the transcontinental railroad.

Kernel Methods for Remote Sensing Data Analysis

Author : Gustau Camps-Valls
Publisher : John Wiley & Sons
ISBN 13 : 0470749008
Total Pages : 434 pages
Book Rating : 4.4/5 (77 download)

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Book Synopsis Kernel Methods for Remote Sensing Data Analysis by : Gustau Camps-Valls

Download or read book Kernel Methods for Remote Sensing Data Analysis written by Gustau Camps-Valls and published by John Wiley & Sons. This book was released on 2009-09-03 with total page 434 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Kernel-based Data Fusion for Machine Learning

Author : Shi Yu
Publisher : Springer
ISBN 13 : 3642194060
Total Pages : 223 pages
Book Rating : 4.6/5 (421 download)

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Book Synopsis Kernel-based Data Fusion for Machine Learning by : Shi Yu

Download or read book Kernel-based Data Fusion for Machine Learning written by Shi Yu and published by Springer. This book was released on 2011-03-29 with total page 223 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Learning Kernel Classifiers

Author : Ralf Herbrich
Publisher : MIT Press
ISBN 13 : 0262546590
Total Pages : 393 pages
Book Rating : 4.2/5 (625 download)

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Book Synopsis Learning Kernel Classifiers by : Ralf Herbrich

Download or read book Learning Kernel Classifiers written by Ralf Herbrich and published by MIT Press. This book was released on 2022-11-01 with total page 393 pages. Available in PDF, EPUB and Kindle. Book excerpt: An overview of the theory and application of kernel classification methods. Linear classifiers in kernel spaces have emerged as a major topic within the field of machine learning. The kernel technique takes the linear classifier—a limited, but well-established and comprehensively studied model—and extends its applicability to a wide range of nonlinear pattern-recognition tasks such as natural language processing, machine vision, and biological sequence analysis. This book provides the first comprehensive overview of both the theory and algorithms of kernel classifiers, including the most recent developments. It begins by describing the major algorithmic advances: kernel perceptron learning, kernel Fisher discriminants, support vector machines, relevance vector machines, Gaussian processes, and Bayes point machines. Then follows a detailed introduction to learning theory, including VC and PAC-Bayesian theory, data-dependent structural risk minimization, and compression bounds. Throughout, the book emphasizes the interaction between theory and algorithms: how learning algorithms work and why. The book includes many examples, complete pseudo code of the algorithms presented, and an extensive source code library.

Machine Learning Methods in the Environmental Sciences

Author : William W. Hsieh
Publisher : Cambridge University Press
ISBN 13 : 0521791928
Total Pages : 364 pages
Book Rating : 4.5/5 (217 download)

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Book Synopsis Machine Learning Methods in the Environmental Sciences by : William W. Hsieh

Download or read book Machine Learning Methods in the Environmental Sciences written by William W. Hsieh and published by Cambridge University Press. This book was released on 2009-07-30 with total page 364 pages. Available in PDF, EPUB and Kindle. Book excerpt: A graduate textbook that provides a unified treatment of machine learning methods and their applications in the environmental sciences.

An Introduction to Support Vector Machines and Other Kernel-based Learning Methods

Author : Nello Cristianini
Publisher : Cambridge University Press
ISBN 13 : 9780521780193
Total Pages : 216 pages
Book Rating : 4.7/5 (81 download)

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Book Synopsis An Introduction to Support Vector Machines and Other Kernel-based Learning Methods by : Nello Cristianini

Download or read book An Introduction to Support Vector Machines and Other Kernel-based Learning Methods written by Nello Cristianini and published by Cambridge University Press. This book was released on 2000-03-23 with total page 216 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is a comprehensive introduction to Support Vector Machines, a generation learning system based on advances in statistical learning theory.

Regularization, Optimization, Kernels, and Support Vector Machines

Author : Johan A.K. Suykens
Publisher : CRC Press
ISBN 13 : 1482241390
Total Pages : 528 pages
Book Rating : 4.4/5 (822 download)

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Book Synopsis Regularization, Optimization, Kernels, and Support Vector Machines by : Johan A.K. Suykens

Download or read book Regularization, Optimization, Kernels, and Support Vector Machines written by Johan A.K. Suykens and published by CRC Press. This book was released on 2014-10-23 with total page 528 pages. Available in PDF, EPUB and Kindle. Book excerpt: Regularization, Optimization, Kernels, and Support Vector Machines offers a snapshot of the current state of the art of large-scale machine learning, providing a single multidisciplinary source for the latest research and advances in regularization, sparsity, compressed sensing, convex and large-scale optimization, kernel methods, and support vector machines. Consisting of 21 chapters authored by leading researchers in machine learning, this comprehensive reference: Covers the relationship between support vector machines (SVMs) and the Lasso Discusses multi-layer SVMs Explores nonparametric feature selection, basis pursuit methods, and robust compressive sensing Describes graph-based regularization methods for single- and multi-task learning Considers regularized methods for dictionary learning and portfolio selection Addresses non-negative matrix factorization Examines low-rank matrix and tensor-based models Presents advanced kernel methods for batch and online machine learning, system identification, domain adaptation, and image processing Tackles large-scale algorithms including conditional gradient methods, (non-convex) proximal techniques, and stochastic gradient descent Regularization, Optimization, Kernels, and Support Vector Machines is ideal for researchers in machine learning, pattern recognition, data mining, signal processing, statistical learning, and related areas.

Machine Learning for Audio, Image and Video Analysis

Author : Francesco Camastra
Publisher : Springer
ISBN 13 : 144716735X
Total Pages : 564 pages
Book Rating : 4.4/5 (471 download)

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Book Synopsis Machine Learning for Audio, Image and Video Analysis by : Francesco Camastra

Download or read book Machine Learning for Audio, Image and Video Analysis written by Francesco Camastra and published by Springer. This book was released on 2015-07-21 with total page 564 pages. Available in PDF, EPUB and Kindle. Book excerpt: This second edition focuses on audio, image and video data, the three main types of input that machines deal with when interacting with the real world. A set of appendices provides the reader with self-contained introductions to the mathematical background necessary to read the book. Divided into three main parts, From Perception to Computation introduces methodologies aimed at representing the data in forms suitable for computer processing, especially when it comes to audio and images. Whilst the second part, Machine Learning includes an extensive overview of statistical techniques aimed at addressing three main problems, namely classification (automatically assigning a data sample to one of the classes belonging to a predefined set), clustering (automatically grouping data samples according to the similarity of their properties) and sequence analysis (automatically mapping a sequence of observations into a sequence of human-understandable symbols). The third part Applications shows how the abstract problems defined in the second part underlie technologies capable to perform complex tasks such as the recognition of hand gestures or the transcription of handwritten data. Machine Learning for Audio, Image and Video Analysis is suitable for students to acquire a solid background in machine learning as well as for practitioners to deepen their knowledge of the state-of-the-art. All application chapters are based on publicly available data and free software packages, thus allowing readers to replicate the experiments.

Digital Signal Processing with Kernel Methods

Author : Jose Luis Rojo-Alvarez
Publisher : John Wiley & Sons
ISBN 13 : 1118611799
Total Pages : 665 pages
Book Rating : 4.1/5 (186 download)

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Book Synopsis Digital Signal Processing with Kernel Methods by : Jose Luis Rojo-Alvarez

Download or read book Digital Signal Processing with Kernel Methods written by Jose Luis Rojo-Alvarez and published by John Wiley & Sons. This book was released on 2018-02-05 with total page 665 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Gaussian Processes for Machine Learning

Author : Carl Edward Rasmussen
Publisher : MIT Press
ISBN 13 : 026218253X
Total Pages : 266 pages
Book Rating : 4.2/5 (621 download)

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Book Synopsis Gaussian Processes for Machine Learning by : Carl Edward Rasmussen

Download or read book Gaussian Processes for Machine Learning written by Carl Edward Rasmussen and published by MIT Press. This book was released on 2005-11-23 with total page 266 pages. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive and self-contained introduction to Gaussian processes, which provide a principled, practical, probabilistic approach to learning in kernel machines. Gaussian processes (GPs) provide a principled, practical, probabilistic approach to learning in kernel machines. GPs have received increased attention in the machine-learning community over the past decade, and this book provides a long-needed systematic and unified treatment of theoretical and practical aspects of GPs in machine learning. The treatment is comprehensive and self-contained, targeted at researchers and students in machine learning and applied statistics. The book deals with the supervised-learning problem for both regression and classification, and includes detailed algorithms. A wide variety of covariance (kernel) functions are presented and their properties discussed. Model selection is discussed both from a Bayesian and a classical perspective. Many connections to other well-known techniques from machine learning and statistics are discussed, including support-vector machines, neural networks, splines, regularization networks, relevance vector machines and others. Theoretical issues including learning curves and the PAC-Bayesian framework are treated, and several approximation methods for learning with large datasets are discussed. The book contains illustrative examples and exercises, and code and datasets are available on the Web. Appendixes provide mathematical background and a discussion of Gaussian Markov processes.

Kernels for Vector-Valued Functions

Author : Mauricio A. Álvarez
Publisher : Foundations & Trends
ISBN 13 : 9781601985583
Total Pages : 86 pages
Book Rating : 4.9/5 (855 download)

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Book Synopsis Kernels for Vector-Valued Functions by : Mauricio A. Álvarez

Download or read book Kernels for Vector-Valued Functions written by Mauricio A. Álvarez and published by Foundations & Trends. This book was released on 2012 with total page 86 pages. Available in PDF, EPUB and Kindle. Book excerpt: This monograph reviews different methods to design or learn valid kernel functions for multiple outputs, paying particular attention to the connection between probabilistic and regularization methods.