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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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- EditorialSpringer
- Año de publicación2013
- ISBN 10 3642267513
- ISBN 13 9783642267512
- EncuadernaciónTapa blanda
- IdiomaInglés
- Número de páginas228
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Kernel-based Data Fusion for Machine Learning
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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 - 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
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Springer Berlin Heidelberg, 2013
ISBN 10: 3642267513
ISBN 13: 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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Calificación del vendedor: 5 de 5 estrellas
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: 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: Methods and Applications in Bioinformatics and Text Mining (Studies in Computational Intelligence, 345)
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