Buch, Englisch, Band 800, 423 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 846 g
Buch, Englisch, Band 800, 423 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 846 g
Reihe: Studies in Computational Intelligence
ISBN: 978-3-030-02383-6
Verlag: Springer International Publishing
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.”
Zielgruppe
Research
Autoren/Hrsg.
Fachgebiete
Weitere Infos & Material
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.