Librería: Ria Christie Collections, Uxbridge, Reino Unido
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Librería: GreatBookPricesUK, Woodford Green, Reino Unido
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Librería: Books Puddle, New York, NY, Estados Unidos de America
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Librería: Revaluation Books, Exeter, Reino Unido
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Añadir al carritoHardcover. Condición: Brand New. 248 pages. 9.25x6.10x0.79 inches. In Stock.
Idioma: Inglés
Publicado por Springer International Publishing, 2016
ISBN 10: 3319477587 ISBN 13: 9783319477589
Librería: AHA-BUCH GmbH, Einbeck, Alemania
EUR 106,99
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Añadir al carritoBuch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book provides a general overview of multiple instance learning (MIL), defining the framework and covering the central paradigms. The authors discuss the most important algorithms for MIL such as classification, regression and clustering. With a focus on classification, a taxonomy is set and the most relevant proposals are specified. Efficient algorithms are developed to discover relevant information when working with uncertainty. Key representative applications are included.This book carries out a study of the key related fields of distance metrics and alternative hypothesis. Chapters examine new and developing aspects of MIL such as data reduction for multi-instance problems and imbalanced MIL data. Class imbalance for multi-instance problems is defined at the bag level, a type of representation that utilizes ambiguity due to the fact that bag labels are available, but the labels of the individual instances are not defined.Additionally, multiple instance multiple label learning is explored. This learning framework introduces flexibility and ambiguity in the object representation providing a natural formulation for representing complicated objects. Thus, an object is represented by a bag of instances and is allowed to have associated multiple class labels simultaneously.This book is suitable for developers and engineers working to apply MIL techniques to solve a variety of real-world problems. It is also useful for researchers or students seeking a thorough overview of MIL literature, methods, and tools.
Librería: GreatBookPricesUK, Woodford Green, Reino Unido
EUR 173,26
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Añadir al carritoCondición: As New. Unread book in perfect condition.
Librería: Mispah books, Redhill, SURRE, Reino Unido
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Añadir al carritoHardcover. Condición: Like New. LIKE NEW. SHIPS FROM MULTIPLE LOCATIONS. book.
Librería: GreatBookPrices, Columbia, MD, Estados Unidos de America
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Añadir al carritoCondición: As New. Unread book in perfect condition.
Librería: Brook Bookstore On Demand, Napoli, NA, Italia
EUR 86,24
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Añadir al carritoCondición: new. Questo è un articolo print on demand.
Idioma: Inglés
Publicado por Springer International Publishing Nov 2016, 2016
ISBN 10: 3319477587 ISBN 13: 9783319477589
Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Alemania
EUR 106,99
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Añadir al carritoBuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book provides a general overview of multiple instance learning (MIL), defining the framework and covering the central paradigms. The authors discuss the most important algorithms for MIL such as classification, regression and clustering. With a focus on classification, a taxonomy is set and the most relevant proposals are specified. Efficient algorithms are developed to discover relevant information when working with uncertainty. Key representative applications are included.This book carries out a study of the key related fields of distance metrics and alternative hypothesis. Chapters examine new and developing aspects of MIL such as data reduction for multi-instance problems and imbalanced MIL data. Class imbalance for multi-instance problems is defined at the bag level, a type of representation that utilizes ambiguity due to the fact that bag labels are available, but the labels of the individual instances are not defined.Additionally, multiple instance multiple label learning is explored. This learning framework introduces flexibility and ambiguity in the object representation providing a natural formulation for representing complicated objects. Thus, an object is represented by a bag of instances and is allowed to have associated multiple class labels simultaneously.This book is suitable for developers and engineers working to apply MIL techniques to solve a variety of real-world problems. It is also useful for researchers or students seeking a thorough overview of MIL literature, methods, and tools. 248 pp. Englisch.
Idioma: Inglés
Publicado por Springer International Publishing, 2016
ISBN 10: 3319477587 ISBN 13: 9783319477589
Librería: moluna, Greven, Alemania
EUR 92,27
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Añadir al carritoGebunden. Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Offers a comprehensive overview of multiple instance learning widely used to classify and label texts, pictures, videos and music in the InternetProvides the user with the most relevant algorithms for MIL and the most represen.
Librería: Majestic Books, Hounslow, Reino Unido
EUR 151,82
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Añadir al carritoCondición: New. Print on Demand.
Librería: Biblios, Frankfurt am main, HESSE, Alemania
EUR 149,22
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Añadir al carritoCondición: New. PRINT ON DEMAND.
Idioma: Inglés
Publicado por Springer, Springer Nov 2016, 2016
ISBN 10: 3319477587 ISBN 13: 9783319477589
Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemania
EUR 106,99
Cantidad disponible: 1 disponibles
Añadir al carritoBuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book provides a general overview of multiple instance learning (MIL), defining the framework and covering the central paradigms. The authors discuss the most important algorithms for MIL such as classification, regression and clustering. With a focus on classification, a taxonomy is set and the most relevant proposals are specified. Efficient algorithms are developed to discover relevant information when working with uncertainty. Key representative applications are included.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 248 pp. Englisch.