Empirical Approach to Machine Learning

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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. Itcan 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.*

    New efficient methods for pattern recognition and machine learning in data-rich environments Focuses on automated methods, which can be easily adapted to various applications Covers techniques with high level of autonomy, capable to deal with complex, heterogeneous data streams Discusses key case studies and industrial applications

    Klappentext

    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. Itcan 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.

    Inhalt
    Introduction.- Part I: Theoretical Background.- Brief Introduction to Statistical Machine Learning.- Brief Introduction to Computational Intelligence.- Part II: Theoretical Fundamentals of the Proposed Approach.- Empirical Approach - Introduction.- Empirical Fuzzy Sets and Systems.- Anomaly Detection - Empirical Approach.- Data Partitioning - Empirical Approach.- Autonomous Learning Multi-Model Systems.- Transparent Deep Rule-Based Classifiers.- Part III: Applications of the Proposed Approach.- Applications of Autonomous Anomaly Detection.

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Weitere Informationen

  • Allgemeine Informationen
    • GTIN 09783030132095
    • Auflage Softcover reprint of the original 1st edition 2019
    • Sprache Englisch
    • Genre Allgemeines & Lexika
    • Lesemotiv Verstehen
    • Größe H235mm x B155mm x T25mm
    • Jahr 2019
    • EAN 9783030132095
    • Format Kartonierter Einband
    • ISBN 3030132099
    • Veröffentlichung 10.12.2019
    • Titel Empirical Approach to Machine Learning
    • Autor Xiaowei Gu , Plamen P. Angelov
    • Untertitel Studies in Computational Intelligence 800
    • Gewicht 686g
    • Herausgeber Springer International Publishing
    • Anzahl Seiten 456

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