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Learning with Partially Labeled and Interdependent Data

Authors: Amini, Massih-Reza, Usunier, Nicolas

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  • Presents an overview of statistical learning theory
  • Analyzes two machine learning frameworks, semi-supervised learning with partially labeled data and learning with interdependent data
  • Outlines how these frameworks can support emerging machine learning applications
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eBook 67,40 €
price for France (gross)
  • ISBN 978-3-319-15726-9
  • Digitally watermarked, DRM-free
  • Included format: EPUB, PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover 84,39 €
price for France (gross)
  • ISBN 978-3-319-15725-2
  • Free shipping for individuals worldwide
  • Institutional customers should get in touch with their account manager
  • Covid-19 shipping restrictions
  • Usually ready to be dispatched within 3 to 5 business days, if in stock
Softcover 84,39 €
price for France (gross)
  • ISBN 978-3-319-35390-6
  • Free shipping for individuals worldwide
  • Institutional customers should get in touch with their account manager
  • Covid-19 shipping restrictions
  • Usually ready to be dispatched within 3 to 5 business days, if in stock
About this book

This book develops two key machine learning principles: the semi-supervised paradigm and learning with interdependent data. It reveals new applications, primarily web related, that transgress the classical machine learning framework through learning with interdependent data.

The book traces how the semi-supervised paradigm and the learning to rank paradigm emerged from new web applications, leading to a massive production of heterogeneous textual data. It explains how semi-supervised learning techniques are widely used, but only allow a limited analysis of the information content and thus do not meet the demands of many web-related tasks.

Later chapters deal with the development of learning methods for ranking entities in a large collection with respect to precise information needed. In some cases, learning a ranking function can be reduced to learning a classification function over the pairs of examples. The book proves that this task can be efficiently tackled in a new framework: learning with interdependent data.

Researchers and professionals in machine learning will find these new perspectives and solutions valuable. Learning with Partially Labeled and Interdependent Data is also useful for advanced-level students of computer science, particularly those focused on statistics and learning.

Table of contents (4 chapters)

Table of contents (4 chapters)

Buy this book

eBook 67,40 €
price for France (gross)
  • ISBN 978-3-319-15726-9
  • Digitally watermarked, DRM-free
  • Included format: EPUB, PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover 84,39 €
price for France (gross)
  • ISBN 978-3-319-15725-2
  • Free shipping for individuals worldwide
  • Institutional customers should get in touch with their account manager
  • Covid-19 shipping restrictions
  • Usually ready to be dispatched within 3 to 5 business days, if in stock
Softcover 84,39 €
price for France (gross)
  • ISBN 978-3-319-35390-6
  • Free shipping for individuals worldwide
  • Institutional customers should get in touch with their account manager
  • Covid-19 shipping restrictions
  • Usually ready to be dispatched within 3 to 5 business days, if in stock
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Bibliographic Information

Bibliographic Information
Book Title
Learning with Partially Labeled and Interdependent Data
Authors
Copyright
2015
Publisher
Springer International Publishing
Copyright Holder
Springer International Publishing Switzerland
eBook ISBN
978-3-319-15726-9
DOI
10.1007/978-3-319-15726-9
Hardcover ISBN
978-3-319-15725-2
Softcover ISBN
978-3-319-35390-6
Edition Number
1
Number of Pages
XIII, 106
Number of Illustrations
12 b/w illustrations
Topics