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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Table of contents (15 chapters)
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Front Matter
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Theoretical Background
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Front Matter
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Theoretical Fundamentals of the Proposed Approach
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Front Matter
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Applications of the Proposed Approach
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Front Matter
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Back Matter
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
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.”
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