Learning Theory And Kernel Machines

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Learning Theory And Kernel Machines

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Author : Bernhard Schoelkopf
language : en
Publisher: Springer Science & Business Media
Release Date : 2003-08-11

Learning Theory And Kernel Machines written by Bernhard Schoelkopf and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003-08-11 with Computers categories.

This book constitutes the joint refereed proceedings of the 16th Annual Conference on Computational Learning Theory, COLT 2003, and the 7th Kernel Workshop, Kernel 2003, held in Washington, DC in August 2003. The 47 revised full papers presented together with 5 invited contributions and 8 open problem statements were carefully reviewed and selected from 92 submissions. The papers are organized in topical sections on kernel machines, statistical learning theory, online learning, other approaches, and inductive inference learning.

Learning Theory And Kernel Machines

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Author : Bernhard Scholkopf
language : en
Publisher:
Release Date : 2014-01-15

Learning Theory And Kernel Machines written by Bernhard Scholkopf and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-01-15 with categories.

Learning With Kernels

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Author : Bernhard Scholkopf
language : en
Publisher: MIT Press
Release Date : 2018-06-05

Learning With Kernels written by Bernhard Scholkopf and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-06-05 with Computers categories.

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.

Learning Theory And Kernel Machines

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Author :
language : en
Publisher:
Release Date : 2003

Learning Theory And Kernel Machines written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003 with categories.

Algorithmic Learning Theory

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Author :
language : en
Publisher:
Release Date : 2004

Algorithmic Learning Theory written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004 with Computer algorithms categories.

Neural Networks And Learning Machines

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Author : Simon S. Haykin
language : en
Publisher: Prentice Hall
Release Date : 2009

Neural Networks And Learning Machines written by Simon S. Haykin and has been published by Prentice Hall this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009 with Computers categories.

For graduate-level neural network courses offered in the departments of Computer Engineering, Electrical Engineering, and Computer Science. Renowned for its thoroughness and readability, this well-organized and completely up-to-date text remains the most comprehensive treatment of neural networks from an engineering perspective. Matlab codes used for the computer experiments in the text are available for download at: http: //www.pearsonhighered.com/haykin/ Refocused, revised and renamed to reflect the duality of neural networks and learning machines, this edition recognizes that the subject matter is richer when these topics are studied together. Ideas drawn from neural networks and machine learning are hybridized to perform improved learning tasks beyond the capability of either independently.

Advanced Lectures On Machine Learning

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Author :
language : en
Publisher:
Release Date : 2002

Advanced Lectures On Machine Learning written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2002 with Machine learning categories.

Learning Kernel Classifiers

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Author : Ralf Herbrich
language : en
Publisher: MIT Press
Release Date : 2001-12-07

Learning Kernel Classifiers written by Ralf Herbrich and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2001-12-07 with Computers categories.

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.

Optimization Based Machine Learning And Data Mining

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Author : Edward W. Wild
language : en
Publisher:
Release Date : 2008

Optimization Based Machine Learning And Data Mining written by Edward W. Wild and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008 with categories.

Mathematical Methods Of Statistics

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Author :
language : en
Publisher:
Release Date : 2007

Mathematical Methods Of Statistics written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007 with Mathematical statistics categories.