Multilabel Classification - Problem Analysis, Metrics and Techniques (Hardcover, 1st ed. 2016)

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.

General

Imprint:	Springer International Publishing AG
Country of origin:	Switzerland
Release date:	August 2016
First published:	2016
Authors:	Francisco Herrera • Francisco Charte • Antonio J. Rivera • Maria J. Del Jesus
Dimensions:	235 x 155 x 13mm (L x W x T)
Format:	Hardcover
Pages:	194
Edition:	1st ed. 2016
ISBN-13:	978-3-319-41110-1
Categories:	Books > Computing & IT > Applications of computing > Databases > Data mining Books > Computing & IT > Applications of computing > Artificial intelligence > General
LSN:	3-319-41110-1
Barcode:	9783319411101

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