Multilabel Classification
- Problem Analysis, Metrics and Techniques
- Indbinding:
- Paperback
- Sideantal:
- 194
- Udgivet:
- 22. april 2018
- Udgave:
- 12016
- Størrelse:
- 155x235x0 mm.
- Vægt:
- 3285 g.
- 8-11 hverdage.
- 20. november 2024
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- 20 timers lytning og læsning
- Adgang til 70.000+ titler
- Ingen binding
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Ingen binding og kan opsiges når som helst.
- 1 valgfrit digitalt ugeblad
- 20 timers lytning og læsning
- Adgang til 70.000+ titler
- Ingen binding
Abonnementet koster 75 kr./md.
Ingen binding og kan opsiges når som helst.
Beskrivelse af Multilabel Classification
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.
. 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.
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