Kernels For Structured Data
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Kernels For Structured Data
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Author :
language : en
Publisher:
Release Date :
Kernels For Structured Data written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on with categories.
Kernels For Structured Data
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Author : Thomas Gartner
language : en
Publisher: World Scientific
Release Date : 2008
Kernels For Structured Data written by Thomas Gartner and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008 with Computers categories.
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.
Kernels For Structured Data
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Author : Thomas Grtner
language : en
Publisher: World Scientific
Release Date : 2008
Kernels For Structured Data written by Thomas Grtner and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008 with Computers categories.
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.
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.
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.
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.
Inductive Logic Programming
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Author : Stan Matwin
language : en
Publisher: Springer Science & Business Media
Release Date : 2003-02-12
Inductive Logic Programming written by Stan Matwin 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 2003-02-12 with Computers categories.
This book constitutes the thoroughly refereed post-proceedings of the 12th International Conference on Inductive Logic Programming, ILP 2002, held in Sydney, Australia in July 2002. The 22 revised full papers presented were carefully selected during two rounds of reviewing and revision from 45 submissions. Among the topics addressed are first order decision lists, learning with description logics, bagging in ILP, kernel methods, concept learning, relational learners, description logic programs, Bayesian classifiers, knowledge discovery, data mining, logical sequences, theory learning, stochastic logic programs, machine discovery, and relational pattern discovery.
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
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Author :
language : en
Publisher: IOS Press
Release Date :
written by and has been published by IOS Press this book supported file pdf, txt, epub, kindle and other format this book has been release on with categories.
Inductive Logic Programming
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Author : Stan Matwin
language : en
Publisher: Springer Science & Business Media
Release Date : 2003-02-12
Inductive Logic Programming written by Stan Matwin 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 2003-02-12 with Computers categories.
This book constitutes the thoroughly refereed post-proceedings of the 12th International Conference on Inductive Logic Programming, ILP 2002, held in Sydney, Australia in July 2002. The 22 revised full papers presented were carefully selected during two rounds of reviewing and revision from 45 submissions. Among the topics addressed are first order decision lists, learning with description logics, bagging in ILP, kernel methods, concept learning, relational learners, description logic programs, Bayesian classifiers, knowledge discovery, data mining, logical sequences, theory learning, stochastic logic programs, machine discovery, and relational pattern discovery.
Predicting Structured Data
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Author : Neural Information Processing Systems Foundation
language : en
Publisher: MIT Press
Release Date : 2007
Predicting Structured Data written by Neural Information Processing Systems Foundation and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007 with Algorithms categories.
State-of-the-art algorithms and theory in a novel domain of machine learning, prediction when the output has structure.