Overview
- 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
Part of the book series: Studies in Computational Intelligence (SCI, volume 800)
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About this book
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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Keywords
- 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
Table of contents (15 chapters)
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Theoretical Background
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Theoretical Fundamentals of the Proposed Approach
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Applications of the Proposed Approach
Authors and Affiliations
Bibliographic Information
Book Title: Empirical Approach to Machine Learning
Authors: Plamen P. Angelov, Xiaowei Gu
Series Title: Studies in Computational Intelligence
DOI: https://doi.org/10.1007/978-3-030-02384-3
Publisher: Springer Cham
eBook Packages: Intelligent Technologies and Robotics, Intelligent Technologies and Robotics (R0)
Copyright Information: Springer Nature Switzerland AG 2019
Hardcover ISBN: 978-3-030-02383-6Published: 25 October 2018
Softcover ISBN: 978-3-030-13209-5Published: 10 December 2019
eBook ISBN: 978-3-030-02384-3Published: 17 October 2018
Series ISSN: 1860-949X
Series E-ISSN: 1860-9503
Edition Number: 1
Number of Pages: XXIX, 423
Number of Illustrations: 49 b/w illustrations, 90 illustrations in colour
Topics: Computational Intelligence, Pattern Recognition, Big Data, Data Mining and Knowledge Discovery, Complexity