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Empirical Approach to Machine Learning

Research output: Book/Report/ProceedingsBook

Publication date2019
Place of PublicationCham
Number of pages437
ISBN (electronic)9783030023843
ISBN (print)9783030023836
<mark>Original language</mark>English

Publication series

NameStudies in Computational Intelligence
PublisherSpringer International Publishing
ISSN (Print)1860-949X
ISSN (electronic)1860-9503


This book provides a ‘one-stop source’ for all readers who are interested in a new, empirical approach to machine learning that, unlike traditional methods, successfully addresses the demands of today’s data-driven world. After an introduction to the fundamentals, the book discusses in depth anomaly detection, data partitioning and clustering, as well as classification and predictors. It describes classifiers of zero and first order, and the new, highly efficient and transparent deep rule-based classifiers, particularly highlighting their applications to image processing. Local optimality and stability conditions for the methods presented are formally derived and stated, while the software is also provided as supplemental, open-source material. The book will greatly benefit postgraduate students, researchers and practitioners dealing with advanced data processing, applied mathematicians, software developers of agent-oriented systems, and developers of embedded and real-time systems. It can also be used as a textbook for postgraduate coursework; for this purpose, a standalone set of lecture notes and corresponding lab session notes are available on the same website as the code.