Kernel Methods And Machine Learning
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Kernel Methods And Machine Learning
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Author : S. Y. Kung
language : en
Publisher: Cambridge University Press
Release Date : 2014-04-17
Kernel Methods And Machine Learning written by S. Y. Kung and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-04-17 with Computers categories.
Covering the fundamentals of kernel-based learning theory, this is an essential resource for graduate students and professionals in computer science.
Learning With Kernels
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Author : Bernhard Schölkopf
language : en
Publisher: MIT Press
Release Date : 2002
Learning With Kernels written by Bernhard Schölkopf and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2002 with Computers categories.
A comprehensive introduction to Support Vector Machines and related kernel methods.
Kernel Methods For Pattern Analysis
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Author : John Shawe-Taylor
language : en
Publisher: Cambridge University Press
Release Date : 2004-06-28
Kernel Methods For Pattern Analysis written by John Shawe-Taylor and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004-06-28 with Computers categories.
Publisher Description
Kernel Methods In Computational Biology
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Author : Bernhard Schölkopf
language : en
Publisher: MIT Press
Release Date : 2004
Kernel Methods In Computational Biology written by Bernhard Schölkopf and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004 with Computers categories.
A detailed overview of current research in kernel methods and their application to computational biology.
Digital Signal Processing With Kernel Methods
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Author : Jose Luis Rojo-Alvarez
language : en
Publisher: John Wiley & Sons
Release Date : 2018-02-05
Digital Signal Processing With Kernel Methods written by Jose Luis Rojo-Alvarez and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-02-05 with Technology & Engineering categories.
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.
An Introduction To Support Vector Machines And Other Kernel Based Learning Methods
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Author : Nello Cristianini
language : en
Publisher: Cambridge University Press
Release Date : 2000-03-23
An Introduction To Support Vector Machines And Other Kernel Based Learning Methods written by Nello Cristianini and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2000-03-23 with Computers categories.
This is a comprehensive introduction to Support Vector Machines, a generation learning system based on advances in statistical learning theory.
Gaussian Processes For Machine Learning
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Author : Carl Edward Rasmussen
language : en
Publisher: MIT Press
Release Date : 2005-11-23
Gaussian Processes For Machine Learning written by Carl Edward Rasmussen and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005-11-23 with Computers categories.
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.
Machine Learning With Svm And Other Kernel Methods
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Author : K.P. Soman
language : en
Publisher:
Release Date : 2011
Machine Learning With Svm And Other Kernel Methods written by K.P. Soman and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011 with Neural networks (Computer science) categories.
Kernels For Vector Valued Functions
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Author : Mauricio A. Álvarez
language : en
Publisher: Foundations & Trends
Release Date : 2012
Kernels For Vector Valued Functions written by Mauricio A. Álvarez and has been published by Foundations & Trends this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012 with Computers categories.
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.
Kernel Mean Embedding Of Distributions
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Author : Krikamol Muandet
language : en
Publisher:
Release Date : 2017-06-28
Kernel Mean Embedding Of Distributions written by Krikamol Muandet and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-06-28 with Computers categories.
Provides a comprehensive review of kernel mean embeddings of distributions and, in the course of doing so, discusses some challenging issues that could potentially lead to new research directions. The targeted audience includes graduate students and researchers in machine learning and statistics.