Téléchargez le livre : Learning with Partially Labeled and Interdependent Data

Learning with Partially Labeled and Interdependent Data



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Springer


Paru le : 2015-05-07



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Description

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.
Pages
106 pages
Collection
n.c
Parution
2015-05-07
Marque
Springer
EAN papier
9783319157252
EAN PDF
9783319157269
Informations sur l'ebook
Nombre pages copiables
1
Nombre pages imprimables
10
Taille du fichier
1792 Ko
Ce livre est accessible aux handicaps Voir les informations sur l'accessibilité
Prix
52,74 €
EAN EPUB
9783319157269
Informations sur l'ebook
Nombre pages copiables
1
Nombre pages imprimables
10
Taille du fichier
1710 Ko
Ce livre est accessible aux handicaps Voir les informations sur l'accessibilité
Prix
52,74 €

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