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Similarity between objects plays an important role in both human
cognitive processes and artificial systems for recognition and
categorization. How to appropriately measure such similarities for
a given task is crucial to the performance of many machine
learning, pattern recognition and data mining methods. This book is
devoted to metric learning, a set of techniques to automatically
learn similarity and distance functions from data that has
attracted a lot of interest in machine learning and related fields
in the past ten years. In this book, we provide a thorough review
of the metric learning literature that covers algorithms, theory
and applications for both numerical and structured data. We first
introduce relevant definitions and classic metric functions, as
well as examples of their use in machine learning and data mining.
We then review a wide range of metric learning algorithms, starting
with the simple setting of linear distance and similarity learning.
We show how one may scale-up these methods to very large amounts of
training data. To go beyond the linear case, we discuss methods
that learn nonlinear metrics or multiple linear metrics throughout
the feature space, and review methods for more complex settings
such as multi-task and semi-supervised learning. Although most of
the existing work has focused on numerical data, we cover the
literature on metric learning for structured data like strings,
trees, graphs and time series. In the more technical part of the
book, we present some recent statistical frameworks for analyzing
the generalization performance in metric learning and derive
results for some of the algorithms presented earlier. Finally, we
illustrate the relevance of metric learning in real-world problems
through a series of successful applications to computer vision,
bioinformatics and information retrieval. Table of Contents:
Introduction / Metrics / Properties of Metric Learning Algorithms /
Linear Metric Learning / Nonlinear and Local Metric Learning /
Metric Learning for Special Settings / Metric Learning for
Structured Data / Generalization Guarantees for Metric Learning /
Applications / Conclusion / Bibliography / Authors' Biographies
Advances in Domain Adaptation Theory gives current,
state-of-the-art results on transfer learning, with a particular
focus placed on domain adaptation from a theoretical point-of-view.
The book begins with a brief overview of the most popular concepts
used to provide generalization guarantees, including sections on
Vapnik-Chervonenkis (VC), Rademacher, PAC-Bayesian, Robustness and
Stability based bounds. In addition, the book explains domain
adaptation problem and describes the four major families of
theoretical results that exist in the literature, including the
Divergence based bounds. Next, PAC-Bayesian bounds are discussed,
including the original PAC-Bayesian bounds for domain adaptation
and their updated version. Additional sections present
generalization guarantees based on the robustness and stability
properties of the learning algorithm.
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