Kernel-Based Data Fusion for Machine Learning: Methods and Applications in Bioinformatics and Text Mining: 345 (Studies in Computational Intelligence) - Tapa dura
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.
"Sinopsis" puede pertenecer a otra edición de este libro.
De la contraportada
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.
"Sobre este título" puede pertenecer a otra edición de este libro.
Otras ediciones populares con el mismo título
Resultados de la búsqueda para Kernel-Based Data Fusion for Machine Learning: Methods...
Imagen de archivo
Kernel-based Data Fusion for Machine Learning: Methods and Applications in Bioinformatics and Text Mining (Studies in Computational Intelligence, 345)
Librería: Lucky's Textbooks, Dallas, TX, Estados Unidos de America
Calificación del vendedor: 5 de 5 estrellas
Condición: New. Nº de ref. del artículo: ABLIING23Mar3113020220323
Comprar nuevo
EUR 186,01
Gastos de envío:
EUR 3,44
A Estados Unidos de America
Cantidad disponible: Más de 20 disponibles
Imagen de archivo
Kernel-based Data Fusion for Machine Learning: Methods and Applications in Bioinformatics and Text Mining (Studies in Computational Intelligence, 345)
Librería: Ria Christie Collections, Uxbridge, Reino Unido
Calificación del vendedor: 5 de 5 estrellas
Condición: New. In. Nº de ref. del artículo: ria9783642194054_new
Comprar nuevo
EUR 180,52
Gastos de envío:
EUR 13,62
De Reino Unido a Estados Unidos de America
Cantidad disponible: Más de 20 disponibles
Imagen del vendedor
Kernel-based Data Fusion for Machine Learning
Publicado por
Springer Berlin Heidelberg, 2011
ISBN 10: 3642194052
ISBN 13: 9783642194054
Nuevo
Tapa dura
Librería: moluna, Greven, Alemania
Calificación del vendedor: 4 de 5 estrellas
Gebunden. Condición: New. Nº de ref. del artículo: 5051868
Comprar nuevo
EUR 153,73
Gastos de envío:
EUR 48,99
De Alemania a Estados Unidos de America
Cantidad disponible: Más de 20 disponibles
Imagen del vendedor
Kernel-based Data Fusion for Machine Learning
Publicado por
Springer Berlin Heidelberg Mrz 2011, 2011
ISBN 10: 3642194052
ISBN 13: 9783642194054
Nuevo
Tapa dura
Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Alemania
Calificación del vendedor: 5 de 5 estrellas
Buch. 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: 9783642194054
Comprar nuevo
EUR 181,89
Gastos de envío:
EUR 23,00
De Alemania a Estados Unidos de America
Cantidad disponible: 2 disponibles
Imagen de archivo
Kernel-based Data Fusion for Machine Learning: Methods and Applications in Bioinformatics and Text Mining (Studies in Computational Intelligence)
Librería: Mispah books, Redhill, SURRE, Reino Unido
Calificación del vendedor: 4 de 5 estrellas
Hardcover. Condición: Like New. Like New. book. Nº de ref. del artículo: ERICA77336421940526
Comprar usado
EUR 192,09
Gastos de envío:
EUR 28,43
De Reino Unido a Estados Unidos de America
Cantidad disponible: 1 disponibles
Imagen de archivo
Kernel-based Data Fusion for Machine Learning: Methods and Applications in Bioinformatics and Text Mining (Studies in Computational Intelligence, 345)
Librería: California Books, Miami, FL, Estados Unidos de America
Calificación del vendedor: 5 de 5 estrellas
Condición: New. Nº de ref. del artículo: I-9783642194054
Comprar nuevo
EUR 229,02
Gastos de envío:
GRATIS
A Estados Unidos de America
Cantidad disponible: Más de 20 disponibles
Imagen del vendedor
Kernel-based Data Fusion for Machine Learning | Methods and Applications in Bioinformatics and Text Mining
Impresión bajo demandaLibrería: preigu, Osnabrück, Alemania
Calificación del vendedor: 5 de 5 estrellas
Buch. Condición: Neu. Kernel-based Data Fusion for Machine Learning | Methods and Applications in Bioinformatics and Text Mining | Shi Yu (u. a.) | Buch | xiv | Englisch | 2011 | Springer-Verlag GmbH | EAN 9783642194054 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu Print on Demand. Nº de ref. del artículo: 107064440
Comprar nuevo
EUR 159,25
Gastos de envío:
EUR 70,00
De Alemania a Estados Unidos de America
Cantidad disponible: 5 disponibles
Imagen de archivo
Kernel-based Data Fusion for Machine Learning : Methods and Applications in Bioinformatics and Text Mining
Publicado por
Springer Berlin Heidelberg, 2011
ISBN 10: 3642194052
ISBN 13: 9783642194054
Antiguo o usado
Tapa dura
Librería: Buchpark, Trebbin, Alemania
Calificación del vendedor: 5 de 5 estrellas
Condición: Gut. Zustand: Gut | Sprache: Englisch | Produktart: Bücher. Nº de ref. del artículo: 10541053/13
Comprar usado
EUR 136,49
Gastos de envío:
EUR 105,00
De Alemania a Estados Unidos de America
Cantidad disponible: 1 disponibles
Imagen del vendedor
Kernel-based Data Fusion for Machine Learning
Publicado por
Springer Berlin Heidelberg, Springer Berlin Heidelberg Mär 2011, 2011
ISBN 10: 3642194052
ISBN 13: 9783642194054
Nuevo
Tapa dura
Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemania
Calificación del vendedor: 5 de 5 estrellas
Buch. Condición: Neu. 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: 9783642194054
Comprar nuevo
EUR 181,89
Gastos de envío:
EUR 60,00
De Alemania a Estados Unidos de America
Cantidad disponible: 2 disponibles
Imagen del vendedor
Kernel-based Data Fusion for Machine Learning : Methods and Applications in Bioinformatics and Text Mining
Publicado por
Springer Berlin Heidelberg, 2011
ISBN 10: 3642194052
ISBN 13: 9783642194054
Nuevo
Tapa dura
Librería: AHA-BUCH GmbH, Einbeck, Alemania
Calificación del vendedor: 5 de 5 estrellas
Buch. 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: 9783642194054
Comprar nuevo
EUR 181,89
Gastos de envío:
EUR 62,77
De Alemania a Estados Unidos de America
Cantidad disponible: 1 disponibles