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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
The term Federated Learning was coined as recently as 2016 to
describe a machine learning setting where multiple entities
collaborate in solving a machine learning problem, under the
coordination of a central server or service provider. Each client's
raw data is stored locally and not exchanged or transferred;
instead, focused updates intended for immediate aggregation are
used to achieve the learning objective. Since then, the topic has
gathered much interest across many different disciplines and the
realization that solving many of these interdisciplinary problems
likely requires not just machine learning but techniques from
distributed optimization, cryptography, security, differential
privacy, fairness, compressed sensing, systems, information theory,
statistics, and more. This monograph has contributions from leading
experts across the disciplines, who describe the latest
state-of-the art from their perspective. These contributions have
been carefully curated into a comprehensive treatment that enables
the reader to understand the work that has been done and get
pointers to where effort is required to solve many of the problems
before Federated Learning can become a reality in practical
systems. Researchers working in the area of distributed systems
will find this monograph an enlightening read that may inspire them
to work on the many challenging issues that are outlined. This
monograph will get the reader up to speed quickly and easily on
what is likely to become an increasingly important topic: Federated
Learning.
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