Verwandte Artikel zu Multiple Instance Learning: Foundations and Algorithms

Multiple Instance Learning: Foundations and Algorithms - Softcover

 
9783319838151: Multiple Instance Learning: Foundations and Algorithms

Inhaltsangabe

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.


Die Inhaltsangabe kann sich auf eine andere Ausgabe dieses Titels beziehen.

Reseña del editor

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.

„Über diesen Titel“ kann sich auf eine andere Ausgabe dieses Titels beziehen.

  • VerlagSpringer
  • Erscheinungsdatum2018
  • ISBN 10 3319838156
  • ISBN 13 9783319838151
  • EinbandTapa blanda
  • SpracheEnglisch
  • Anzahl der Seiten248
  • Kontakt zum HerstellerNicht verfügbar

Gratis für den Versand innerhalb von/der Deutschland

Versandziele, Kosten & Dauer

Weitere beliebte Ausgaben desselben Titels

9783319477589: Multiple Instance Learning: Foundations and Algorithms

Vorgestellte Ausgabe

ISBN 10:  3319477587 ISBN 13:  9783319477589
Verlag: Springer, 2016
Hardcover

Suchergebnisse für Multiple Instance Learning: Foundations and Algorithms

Foto des Verkäufers

Francisco Herrera
ISBN 10: 3319838156 ISBN 13: 9783319838151
Neu Taschenbuch

Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland

Verkäuferbewertung 5 von 5 Sternen 5 Sterne, Erfahren Sie mehr über Verkäufer-Bewertungen

Taschenbuch. Zustand: 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. Artikel-Nr. 9783319838151

Verkäufer kontaktieren

Neu kaufen

EUR 106,99
Währung umrechnen
Versand: Gratis
Innerhalb Deutschlands
Versandziele, Kosten & Dauer

Anzahl: 1 verfügbar

In den Warenkorb

Beispielbild für diese ISBN

Herrera, Francisco
Verlag: Springer, 2018
ISBN 10: 3319838156 ISBN 13: 9783319838151
Neu Softcover

Anbieter: Ria Christie Collections, Uxbridge, Vereinigtes Königreich

Verkäuferbewertung 5 von 5 Sternen 5 Sterne, Erfahren Sie mehr über Verkäufer-Bewertungen

Zustand: New. In. Artikel-Nr. ria9783319838151_new

Verkäufer kontaktieren

Neu kaufen

EUR 119,28
Währung umrechnen
Versand: EUR 5,91
Von Vereinigtes Königreich nach Deutschland
Versandziele, Kosten & Dauer

Anzahl: Mehr als 20 verfügbar

In den Warenkorb