Multilabel Classification: Problem Analysis, Metrics and Techniques - Tapa blanda

Herrera, Francisco; Charte, Francisco; Rivera, Antonio J.; Del Jesus, María J.

 
9783319822693: Multilabel Classification: Problem Analysis, Metrics and Techniques
Tapa blanda
ISBN 10: 3319822691 ISBN 13: 9783319822693
Editorial: Springer, 2018

Sinopsis

This book offers a comprehensive review of multilabel techniques widely used to classify and label texts, pictures, videos and music in the Internet. A deep review of the specialized literature on the field includes the available software needed to work with this kind of data. It provides the user with the software tools needed to deal with multilabel data, as well as step by step instruction on how to use them. The main topics covered are:

• The special characteristics of multi-labeled data and the metrics available to measure them.
• The importance of taking advantage of label correlations to improve the results.
• The different approaches followed to face multi-label classification.
• The preprocessing techniques applicable to multi-label datasets.
• The available software tools to work with multi-label data.

This book is beneficial for professionals and researchers in a variety of fields because of the wide range of potential applications for multilabel classification. Besides its multiple applications to classify different types of online information, it is also useful in many other areas, such as genomics and biology. No previous knowledge about the subject is required. The book introduces all the needed concepts to understand multilabel data characterization, treatment and evaluation.

"Sinopsis" puede pertenecer a otra edición de este libro.

Reseña del editor

This book offers a comprehensive review of multilabel techniques widely used to classify and label texts, pictures, videos and music in the Internet. A deep review of the specialized literature on the field includes the available software needed to work with this kind of data. It provides the user with the software tools needed to deal with multilabel data, as well as step by step instruction on how to use them. The main topics covered are:
· The special characteristics of multi-labeled data and the metrics available to measure them.· The importance of taking advantage of label correlations to improve the results.· The different approaches followed to face multi-label classification.· The preprocessing techniques applicable to multi-label datasets.· The available software tools to work with multi-label data.
This book is beneficial for professionals and researchers in a variety of fields because of the wide range of potential applications for multilabel classification. Besides its multiple applications to classify different types of online information, it is also useful in many other areas, such as genomics and biology. No previous knowledge about the subject is required. The book introduces all the needed concepts to understand multilabel data characterization, treatment and evaluation.

"Sobre este título" puede pertenecer a otra edición de este libro.

Editorial
Springer
Año de publicación
2018
Idioma
Inglés
ISBN 10 
3319822691
ISBN 13 
9783319822693
Encuadernación
Tapa blanda
Número de páginas
212
Contacto del fabricante
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Heidelberg
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Alemania

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9783319411101: Multilabel Classification: Problem Analysis, Metrics and Techniques

Edición Destacada

ISBN 10:  3319411101 ISBN 13:  9783319411101
Editorial: Springer, 2016
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