The Innermost Kernel

The Innermost Kernel

Author: Suzanne Gieser

Publisher: Springer Science & Business Media

Published: 2005-02-14

Total Pages: 424

ISBN-13: 9783540208563

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The publication of W. Pauli's Scientific Correspondence by Springer-Verlag has motivated a vast research activity on Pauli's role in modern science. This excellent treatise sheds light on the ongoing dialogue between physics and psychology.


Book Synopsis The Innermost Kernel by : Suzanne Gieser

Download or read book The Innermost Kernel written by Suzanne Gieser and published by Springer Science & Business Media. This book was released on 2005-02-14 with total page 424 pages. Available in PDF, EPUB and Kindle. Book excerpt: The publication of W. Pauli's Scientific Correspondence by Springer-Verlag has motivated a vast research activity on Pauli's role in modern science. This excellent treatise sheds light on the ongoing dialogue between physics and psychology.


Kernel Methods for Pattern Analysis

Kernel Methods for Pattern Analysis

Author: John Shawe-Taylor

Publisher: Cambridge University Press

Published: 2004-06-28

Total Pages: 520

ISBN-13: 1139451618

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Kernel methods provide a powerful and unified framework for pattern discovery, motivating algorithms that can act on general types of data (e.g. strings, vectors or text) and look for general types of relations (e.g. rankings, classifications, regressions, clusters). The application areas range from neural networks and pattern recognition to machine learning and data mining. This book, developed from lectures and tutorials, fulfils two major roles: firstly it provides practitioners with a large toolkit of algorithms, kernels and solutions ready to use for standard pattern discovery problems in fields such as bioinformatics, text analysis, image analysis. Secondly it provides an easy introduction for students and researchers to the growing field of kernel-based pattern analysis, demonstrating with examples how to handcraft an algorithm or a kernel for a new specific application, and covering all the necessary conceptual and mathematical tools to do so.


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: Kernel methods provide a powerful and unified framework for pattern discovery, motivating algorithms that can act on general types of data (e.g. strings, vectors or text) and look for general types of relations (e.g. rankings, classifications, regressions, clusters). The application areas range from neural networks and pattern recognition to machine learning and data mining. This book, developed from lectures and tutorials, fulfils two major roles: firstly it provides practitioners with a large toolkit of algorithms, kernels and solutions ready to use for standard pattern discovery problems in fields such as bioinformatics, text analysis, image analysis. Secondly it provides an easy introduction for students and researchers to the growing field of kernel-based pattern analysis, demonstrating with examples how to handcraft an algorithm or a kernel for a new specific application, and covering all the necessary conceptual and mathematical tools to do so.


Journal of Agricultural Research

Journal of Agricultural Research

Author:

Publisher:

Published: 1942

Total Pages: 854

ISBN-13:

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Book Synopsis Journal of Agricultural Research by :

Download or read book Journal of Agricultural Research written by and published by . This book was released on 1942 with total page 854 pages. Available in PDF, EPUB and Kindle. Book excerpt:


Kernel-based Approximation Methods Using Matlab

Kernel-based Approximation Methods Using Matlab

Author: Gregory E Fasshauer

Publisher: World Scientific Publishing Company

Published: 2015-07-30

Total Pages: 537

ISBN-13: 9814630152

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In an attempt to introduce application scientists and graduate students to the exciting topic of positive definite kernels and radial basis functions, this book presents modern theoretical results on kernel-based approximation methods and demonstrates their implementation in various settings. The authors explore the historical context of this fascinating topic and explain recent advances as strategies to address long-standing problems. Examples are drawn from fields as diverse as function approximation, spatial statistics, boundary value problems, machine learning, surrogate modeling and finance. Researchers from those and other fields can recreate the results within using the documented MATLAB code, also available through the online library. This combination of a strong theoretical foundation and accessible experimentation empowers readers to use positive definite kernels on their own problems of interest.


Book Synopsis Kernel-based Approximation Methods Using Matlab by : Gregory E Fasshauer

Download or read book Kernel-based Approximation Methods Using Matlab written by Gregory E Fasshauer and published by World Scientific Publishing Company. This book was released on 2015-07-30 with total page 537 pages. Available in PDF, EPUB and Kindle. Book excerpt: In an attempt to introduce application scientists and graduate students to the exciting topic of positive definite kernels and radial basis functions, this book presents modern theoretical results on kernel-based approximation methods and demonstrates their implementation in various settings. The authors explore the historical context of this fascinating topic and explain recent advances as strategies to address long-standing problems. Examples are drawn from fields as diverse as function approximation, spatial statistics, boundary value problems, machine learning, surrogate modeling and finance. Researchers from those and other fields can recreate the results within using the documented MATLAB code, also available through the online library. This combination of a strong theoretical foundation and accessible experimentation empowers readers to use positive definite kernels on their own problems of interest.


Historical Theology: An Introduction

Historical Theology: An Introduction

Author: Geoffrey W. Bromiley

Publisher: Wipf and Stock Publishers

Published: 1998-02-04

Total Pages: 495

ISBN-13: 1579101720

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ÒAn ideal historical theology, or even an introduction to it, Ò says Geoffrey Bromiley, Òlies beyond the limits of human possibility.Ó And he does not intend this volume to be an all-inclusive theological study about everybody and everything. Rather, Òthis work is composed for beginners, for inquirers, for those who know nothing or very little of the history of theology, but who want to know something, or something more.


Book Synopsis Historical Theology: An Introduction by : Geoffrey W. Bromiley

Download or read book Historical Theology: An Introduction written by Geoffrey W. Bromiley and published by Wipf and Stock Publishers. This book was released on 1998-02-04 with total page 495 pages. Available in PDF, EPUB and Kindle. Book excerpt: ÒAn ideal historical theology, or even an introduction to it, Ò says Geoffrey Bromiley, Òlies beyond the limits of human possibility.Ó And he does not intend this volume to be an all-inclusive theological study about everybody and everything. Rather, Òthis work is composed for beginners, for inquirers, for those who know nothing or very little of the history of theology, but who want to know something, or something more.


Regularization, Optimization, Kernels, and Support Vector Machines

Regularization, Optimization, Kernels, and Support Vector Machines

Author: Johan A.K. Suykens

Publisher: CRC Press

Published: 2014-10-23

Total Pages: 528

ISBN-13: 1482241390

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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.


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.


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

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

Author: Nello Cristianini

Publisher: Cambridge University Press

Published: 2000-03-23

Total Pages: 216

ISBN-13: 9780521780193

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This is a comprehensive introduction to Support Vector Machines, a generation learning system based on advances in statistical learning theory.


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.


Compiler Construction

Compiler Construction

Author: Michael O'Boyle

Publisher: Springer Science & Business Media

Published: 2012-03-09

Total Pages: 256

ISBN-13: 3642286518

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This book constitutes the proceedings of the 21st International Conference on Compiler Construction, CC 2012, held as part of the joint European Conference on Theory and Practice of Software, ETAPS 2012, which took place in Tallinn, Estonia, in March/April 2012. The 13 papers presented in this book were carefully reviewed and selected from 51 submissions. They are organized in topical sections named: GPU optimisation, program analysis, objects and components, and dynamic analysis and runtime support.


Book Synopsis Compiler Construction by : Michael O'Boyle

Download or read book Compiler Construction written by Michael O'Boyle and published by Springer Science & Business Media. This book was released on 2012-03-09 with total page 256 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the proceedings of the 21st International Conference on Compiler Construction, CC 2012, held as part of the joint European Conference on Theory and Practice of Software, ETAPS 2012, which took place in Tallinn, Estonia, in March/April 2012. The 13 papers presented in this book were carefully reviewed and selected from 51 submissions. They are organized in topical sections named: GPU optimisation, program analysis, objects and components, and dynamic analysis and runtime support.


Kernel Methods in Bioengineering, Signal and Image Processing

Kernel Methods in Bioengineering, Signal and Image Processing

Author: Gustavo Camps-Valls

Publisher: IGI Global

Published: 2007-01-01

Total Pages: 431

ISBN-13: 1599040425

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"This book presents an extensive introduction to the field of kernel methods and real world applications. The book is organized in four parts: the first is an introductory chapter providing a framework of kernel methods; the others address Bioegineering, Signal Processing and Communications and Image Processing"--Provided by publisher.


Book Synopsis Kernel Methods in Bioengineering, Signal and Image Processing by : Gustavo Camps-Valls

Download or read book Kernel Methods in Bioengineering, Signal and Image Processing written by Gustavo Camps-Valls and published by IGI Global. This book was released on 2007-01-01 with total page 431 pages. Available in PDF, EPUB and Kindle. Book excerpt: "This book presents an extensive introduction to the field of kernel methods and real world applications. The book is organized in four parts: the first is an introductory chapter providing a framework of kernel methods; the others address Bioegineering, Signal Processing and Communications and Image Processing"--Provided by publisher.


Kernels for Structured Data

Kernels for Structured Data

Author: Thomas G„rtner

Publisher: World Scientific

Published: 2008

Total Pages: 216

ISBN-13: 9812814558

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This book provides a unique treatment of an important area of machine learning and answers the question of how kernel methods can be applied to structured data. Kernel methods are a class of state-of-the-art learning algorithms that exhibit excellent learning results in several application domains. Originally, kernel methods were developed with data in mind that can easily be embedded in a Euclidean vector space. Much real-world data does not have this property but is inherently structured. An example of such data, often consulted in the book, is the (2D) graph structure of molecules formed by their atoms and bonds. The book guides the reader from the basics of kernel methods to advanced algorithms and kernel design for structured data. It is thus useful for readers who seek an entry point into the field as well as experienced researchers.


Book Synopsis Kernels for Structured Data by : Thomas G„rtner

Download or read book Kernels for Structured Data written by Thomas G„rtner and published by World Scientific. This book was released on 2008 with total page 216 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a unique treatment of an important area of machine learning and answers the question of how kernel methods can be applied to structured data. Kernel methods are a class of state-of-the-art learning algorithms that exhibit excellent learning results in several application domains. Originally, kernel methods were developed with data in mind that can easily be embedded in a Euclidean vector space. Much real-world data does not have this property but is inherently structured. An example of such data, often consulted in the book, is the (2D) graph structure of molecules formed by their atoms and bonds. The book guides the reader from the basics of kernel methods to advanced algorithms and kernel design for structured data. It is thus useful for readers who seek an entry point into the field as well as experienced researchers.