Overview
- Presents an overview of statistical learning theory
- Analyzes two machine learning frameworks, semi-supervised learning with partially labeled data and learning with interdependent data
- Outlines how these frameworks can support emerging machine learning applications
- Includes supplementary material: sn.pub/extras
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Table of contents (4 chapters)
Keywords
About this book
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.
Authors and Affiliations
Bibliographic Information
Book Title: Learning with Partially Labeled and Interdependent Data
Authors: Massih-Reza Amini, Nicolas Usunier
DOI: https://doi.org/10.1007/978-3-319-15726-9
Publisher: Springer Cham
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Springer International Publishing Switzerland 2015
Hardcover ISBN: 978-3-319-15725-2Published: 21 May 2015
Softcover ISBN: 978-3-319-35390-6Published: 09 October 2016
eBook ISBN: 978-3-319-15726-9Published: 07 May 2015
Edition Number: 1
Number of Pages: XIII, 106
Number of Illustrations: 12 b/w illustrations
Topics: Artificial Intelligence, Data Mining and Knowledge Discovery, Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences