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Kernel-based Data Fusion for Machine Learning: Methods and Applications in Bioinformatics and Text Mining: 345 (Studies in Computational Intelligence) - Tapa blanda
Sinopsis
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.
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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.
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Kernel-based Data Fusion for Machine Learning: Methods and Applications in Bioinformatics and Text Mining (Studies in Computational Intelligence, 345)
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Kernel-based Data Fusion for Machine Learning: Methods and Applications in Bioinformatics and Text Mining (Studies in Computational Intelligence, 345)
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Kernel-based Data Fusion for Machine Learning
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Kernel-based Data Fusion for Machine Learning
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Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 228 pp. Englisch. Nº de ref. del artículo: 9783642267512
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Kernel-based Data Fusion for Machine Learning: Methods and Applications in Bioinformatics and Text Mining (Studies in Computational Intelligence, 345)
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Kernel-Based Data Fusion for Machine Learning
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Kernel-based Data Fusion for Machine Learning | Methods and Applications in Bioinformatics and Text Mining
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Taschenbuch. Condición: Neu. Kernel-based Data Fusion for Machine Learning | Methods and Applications in Bioinformatics and Text Mining | Shi Yu (u. a.) | Taschenbuch | xiv | Englisch | 2013 | Springer | EAN 9783642267512 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu. Nº de ref. del artículo: 105964817
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Kernel-based Data Fusion for Machine Learning
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Springer Berlin Heidelberg, Springer Berlin Heidelberg Apr 2013, 2013
ISBN 10: 3642267513
ISBN 13: 9783642267512
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Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 228 pp. Englisch. Nº de ref. del artículo: 9783642267512
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Kernel-based Data Fusion for Machine Learning : Methods and Applications in Bioinformatics and Text Mining
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Springer Berlin Heidelberg, Springer Berlin Heidelberg, 2013
ISBN 10: 3642267513
ISBN 13: 9783642267512
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Taschenbuch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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. Nº de ref. del artículo: 9783642267512
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Kernel-Based Data Fusion for Machine Learning
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