Learning with Partially Labeled and Interdependent Data

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.