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. Dimitar Filev, Henry
Ford Technical Fellow, Ford Motor Company, USA, and Member of the
National Academy of Engineering, USA: "The book Empirical Approach
to Machine Learning opens new horizons to automated and efficient
data processing." Paul J. Werbos, Inventor of the back-propagation
method, USA: "I owe great thanks to Professor Plamen Angelov for
making this important material available to the community just as I
see great practical needs for it, in the new area of making real
sense of high-speed data from the brain." Chin-Teng Lin,
Distinguished Professor at University of Technology Sydney,
Australia: "This new book will set up a milestone for the modern
intelligent systems." Edward Tunstel, President of IEEE Systems,
Man, Cybernetics Society, USA: "Empirical Approach to Machine
Learning provides an insightful and visionary boost of progress in
the evolution of computational learning capabilities yielding
interpretable and transparent implementations."
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