Learning with Partially Labeled and Interdependent Data

Autor: Massih-Reza Amini, Nicolas Usunier

Jazyk:

Väzba: Brožovaná

Vydavateľ: Springer International Publishing AG

Dostupnosť: Skladom u dodávateľa v malom množstve

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

Informácie o knihe

Autor

Massih-Reza Amini, Nicolas Usunier

Jazyk

Angličtina

Väzba

Kniha - Brožovaná

Vydalo

2016

Stránok

106

EAN

9783319353906

ISBN

331935390X

Enbook ID

15193383

Vydavateľ

Springer International Publishing AG

Hmotnosť

1942

Rozmery

155 x 235 x 6

Kompletný 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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