Learning with Partially Labeled and Interdependent Data

Autor: Massih-Reza Amini, Nicolas Usunier

Jazyk:

Vazba: Brožovaná

Vydavatel: Springer International Publishing AG

Dostupnost: Skladem u dodavatele v malém množství

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This book develops two key machine learning principles: the semi-supervised paradigm and learning wi...

Informace o knize

Autor

Massih-Reza Amini, Nicolas Usunier

Jazyk

Angličtina

Vazba

Kniha - Brožovaná

Vydáno

2016

Stránek

106

EAN

9783319353906

ISBN

331935390X

Enbook ID

15193383

Vydavatel

Springer International Publishing AG

Hmotnost

1942

Rozměry

155 x 235 x 6

Kompletní popis

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.

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Learning with Partially Labeled and Interdependent Data

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