Learning with Partially Labeled and Interdependent Data
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Beschreibung
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.
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
Autor*in
Massih-Reza Amini
Themen in »Learning with Partially Labeled and Interdependent Data«
learning to rank learning with interdependent data learning with partially labeled data machine learning multiclass learning multiview learning self-training semi-supervised learning statistical learning theory