Multilabel Classification
Problem Analysis, Metrics and Techniques
María J. del Jesus, Francisco Charte, Francisco Herrera, et al.
PDF
ca. 96,29 €
Amazon
iTunes
Thalia.de
Hugendubel
Bücher.de
ebook.de
kobo
Osiander
Google Books
Barnes&Noble
bol.com
Legimi
yourbook.shop
Kulturkaufhaus
ebooks-center.de
* Affiliatelinks/Werbelinks
* Affiliatelinks/Werbelinks
Hinweis: Affiliatelinks/Werbelinks
Links auf reinlesen.de sind sogenannte Affiliate-Links. Wenn du auf so einen Affiliate-Link klickst und über diesen Link einkaufst, bekommt reinlesen.de von dem betreffenden Online-Shop oder Anbieter eine Provision. Für dich verändert sich der Preis nicht.
Links auf reinlesen.de sind sogenannte Affiliate-Links. Wenn du auf so einen Affiliate-Link klickst und über diesen Link einkaufst, bekommt reinlesen.de von dem betreffenden Online-Shop oder Anbieter eine Provision. Für dich verändert sich der Preis nicht.
Springer International Publishing
Naturwissenschaften, Medizin, Informatik, Technik / Informatik
Beschreibung
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.
Weitere Titel in dieser Kategorie
Kundenbewertungen
Schlagwörter
Learning from imbalanced data, Dataset characterization, Preprocessing, Multi-label data, Data mining software, Data mining, Feature selection, Classification, Dimensionality reduction, Text categorization, Machine learning