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Bücher Wissen & Bildung Englische Bücher Naturwissenschaft, Medizin, Informatik, Technik Technik Allg. & Lexika Empirical Approach to Machine Learning
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Empirical Approach to Machine Learning
Plamen P. Angelov (Gebundene Ausgabe, Englisch)
★★★★★
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Beschreibung
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.” Dieses Produkt haben wir gerade leider nicht auf Lager.
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Technische Daten
Erscheinungsdatum
25.10.2018
Sprache
Englisch
EAN
9783030023836
Herausgeber
Springer International Publishing
Serien- oder Bandtitel
Studies in Computational Intelligence
Sonderedition
Nein
Autor
Plamen P. Angelov
Seitenanzahl
423
Auflage
1st edition 2019
Einbandart
Gebundene Ausgabe
Schlagwörter
Empirical Data Analytics, Data-centered Approaches, Deep Learning Applications, Fuzzy Rule-based Classifiers, Evolving Fuzzy-systems, Neuro-fuzzy Systems, Machine Learning for Cyber Physical Systems, Computational Intelligence for Industry 4.0, AI Methods for Industry, Dealing with Heterogeneous Data Streams, Dealing with Uncertain, Complex Data, Probability Distribution Based Approach, Feature Selection, Anomaly Detection, Data Density in Pattern Recognition, Typicality Distribution Function, Density-based Analytics, Autonomous Learning Systems, Multi-model Systems, Self-evolving Systems
Thema-Inhalt
UYQ - Künstliche Intelligenz
UYQP - Mustererkennung
UN - Datenbanken
UNF - Data Mining
UYQE - Wissensbasierte Systeme, Expertensysteme
TBJ - Mathematik für Ingenieure
GPFC - Kybernetik und Systemtheorie
Höhe
235 mm
Breite
15.5 cm
Hersteller: Birkhäuser, Europaplatz 3, Heidelberg, Deutschland, 69115, ProductSafety@springernature.com, Springer Nature Customer Service Center GmbH
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