Verwandte Artikel zu Kernel-based Data Fusion for Machine Learning: Methods...
Kernel-based Data Fusion for Machine Learning: Methods and Applications in Bioinformatics and Text Mining: 345 (Studies in Computational Intelligence) - Softcover
Inhaltsangabe
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.
Die Inhaltsangabe kann sich auf eine andere Ausgabe dieses Titels beziehen.
Von der hinteren Coverseite
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.
„Über diesen Titel“ kann sich auf eine andere Ausgabe dieses Titels beziehen.
EUR 250,18
EUR 28,78 für den Versand von Vereinigtes Königreich nach USA
Versandziele, Kosten & DauerNeu kaufen
Diesen Artikel anzeigen
EUR 185,00
EUR 3,43 für den Versand innerhalb von/der USA
Versandziele, Kosten & DauerWeitere beliebte Ausgaben desselben Titels
Suchergebnisse für Kernel-based Data Fusion for Machine Learning: Methods...
Beispielbild für diese ISBN
Kernel-based Data Fusion for Machine Learning: Methods and Applications in Bioinformatics and Text Mining (Studies in Computational Intelligence, 345)
Anbieter: Lucky's Textbooks, Dallas, TX, USA
Verkäuferbewertung 5 von 5 Sternen
Zustand: New. Bestandsnummer des Verkäufers ABLIING23Mar3113020222633
Anzahl: Mehr als 20 verfügbar
Beispielbild für diese ISBN
Kernel-based Data Fusion for Machine Learning (Paperback)
ISBN 10: 3642267513
ISBN 13: 9783642267512
Neu
Paperback
Anbieter: Grand Eagle Retail, Bensenville, IL, USA
Verkäuferbewertung 5 von 5 Sternen
Paperback. Zustand: new. Paperback. 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. 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Bestandsnummer des Verkäufers 9783642267512
Anzahl: 1 verfügbar
Beispielbild für diese ISBN
Kernel-based Data Fusion for Machine Learning: Methods and Applications in Bioinformatics and Text Mining (Studies in Computational Intelligence, 345)
Anbieter: Ria Christie Collections, Uxbridge, Vereinigtes Königreich
Verkäuferbewertung 5 von 5 Sternen
Zustand: New. In. Bestandsnummer des Verkäufers ria9783642267512_new
Anzahl: Mehr als 20 verfügbar
Foto des Verkäufers
Kernel-based Data Fusion for Machine Learning
Anbieter: moluna, Greven, Deutschland
Verkäuferbewertung 4 von 5 Sternen
Zustand: New. Bestandsnummer des Verkäufers 5054797
Anzahl: Mehr als 20 verfügbar
Foto des Verkäufers
Kernel-based Data Fusion for Machine Learning
ISBN 10: 3642267513
ISBN 13: 9783642267512
Neu
Taschenbuch
Anbieter: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Deutschland
Verkäuferbewertung 5 von 5 Sternen
Taschenbuch. Zustand: 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. Bestandsnummer des Verkäufers 9783642267512
Anzahl: 2 verfügbar
Beispielbild für diese ISBN
Kernel-Based Data Fusion for Machine Learning
Anbieter: Books Puddle, New York, NY, USA
Verkäuferbewertung 4 von 5 Sternen
Zustand: New. pp. 228. Bestandsnummer des Verkäufers 2648024912
Anzahl: 4 verfügbar
Beispielbild für diese ISBN
Kernel-based Data Fusion for Machine Learning: Methods and Applications in Bioinformatics and Text Mining (Studies in Computational Intelligence, 345)
Anbieter: California Books, Miami, FL, USA
Verkäuferbewertung 5 von 5 Sternen
Zustand: New. Bestandsnummer des Verkäufers I-9783642267512
Anzahl: Mehr als 20 verfügbar
Foto des Verkäufers
Kernel-based Data Fusion for Machine Learning
ISBN 10: 3642267513
ISBN 13: 9783642267512
Neu
Taschenbuch
Anbieter: buchversandmimpf2000, Emtmannsberg, BAYE, Deutschland
Verkäuferbewertung 5 von 5 Sternen
Taschenbuch. Zustand: 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. Bestandsnummer des Verkäufers 9783642267512
Anzahl: 1 verfügbar
Foto des Verkäufers
Kernel-based Data Fusion for Machine Learning : Methods and Applications in Bioinformatics and Text Mining
Verlag:
Springer Berlin Heidelberg, Springer Berlin Heidelberg, 2013
ISBN 10: 3642267513
ISBN 13: 9783642267512
Neu
Taschenbuch
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
Verkäuferbewertung 5 von 5 Sternen
Taschenbuch. Zustand: 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. Bestandsnummer des Verkäufers 9783642267512
Anzahl: 1 verfügbar
Beispielbild für diese ISBN
Kernel-Based Data Fusion for Machine Learning
Print-on-DemandAnbieter: Majestic Books, Hounslow, Vereinigtes Königreich
Verkäuferbewertung 5 von 5 Sternen
Zustand: New. Print on Demand pp. 228 43 Illus. (2 Col.). Bestandsnummer des Verkäufers 44790415
Anzahl: 4 verfügbar