Learning with Partially Labeled and Interdependent Data

Name: Learning with Partially Labeled and Interdependent Data
Brand: Springer International Publishing AG
SKU: 9783319157252
Price: 19305 HUF
Availability: InStock

Szerző: Massih-Reza Amini, Nicolas Usunier

Nyelv:

Kötés: Kemény kötésű

Kiadó: Springer International Publishing AG

Elérhetőség: Beszállítói készleten

Küldés 10-13 napon belül

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

Információk a könyvről

Szerző

Massih-Reza Amini, Nicolas Usunier

Nyelv

Angol

Kötés

Könyv - Kemény kötésű

Kiadva

2015

oldal

106

EAN

9783319157252

ISBN

3319157256

Enbook ID

09193124

Kiadó

Springer International Publishing AG

Súly

354

Méretek

155 x 235 x 245

Teljes leírás

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 framework through learning with interdependent data. The book traces how the semi-supervised and learning to rank paradigms 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 groundwork: learning with interdependent data. It also presents the most valuable algorithms. Researchers and professionals in machine learning will find the 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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