Learning with Partially Labeled and Interdependent Data

Amini, Massih-Reza; Usunier, Nicolas

ISBN 10: 331935390X / ISBN 13: 9783319353906

Published by Springer, 2016

About this Item

pp. 120.

Seller Inventory # 26378184245

Bibliographic Details

• Title: Learning with Partially Labeled and Interdependent Data
• Publisher: Springer
• Publication Date: 2016
• Language: English
• ISBN 10: 331935390X
• ISBN 13: 9783319353906
• Binding: Soft cover
• Condition: New

About this title

Synopsis

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.