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.
