Advertisement

Empirical Approach to Machine Learning

  • Plamen P. Angelov
  • Xiaowei Gu

Part of the Studies in Computational Intelligence book series (SCI, volume 800)

Table of contents

  1. Front Matter
    Pages i-xxix
  2. Plamen P. Angelov, Xiaowei Gu
    Pages 1-14
  3. Theoretical Background

    1. Front Matter
      Pages 15-15
    2. Plamen P. Angelov, Xiaowei Gu
      Pages 17-67
    3. Plamen P. Angelov, Xiaowei Gu
      Pages 69-99
  4. Theoretical Fundamentals of the Proposed Approach

    1. Front Matter
      Pages 101-101
    2. Plamen P. Angelov, Xiaowei Gu
      Pages 103-133
    3. Plamen P. Angelov, Xiaowei Gu
      Pages 135-155
    4. Plamen P. Angelov, Xiaowei Gu
      Pages 157-173
    5. Plamen P. Angelov, Xiaowei Gu
      Pages 175-198
    6. Plamen P. Angelov, Xiaowei Gu
      Pages 199-222
    7. Plamen P. Angelov, Xiaowei Gu
      Pages 223-245
  5. Applications of the Proposed Approach

    1. Front Matter
      Pages 247-247
    2. Plamen P. Angelov, Xiaowei Gu
      Pages 249-259
    3. Plamen P. Angelov, Xiaowei Gu
      Pages 261-276
    4. Plamen P. Angelov, Xiaowei Gu
      Pages 277-293
    5. Plamen P. Angelov, Xiaowei Gu
      Pages 295-319
    6. Plamen P. Angelov, Xiaowei Gu
      Pages 321-340
    7. Plamen P. Angelov, Xiaowei Gu
      Pages 341-346
  6. Back Matter
    Pages 347-423

About this book

Introduction

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. It can 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: “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.”

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

Authors and affiliations

  • Plamen P. Angelov
    • 1
  • Xiaowei Gu
    • 2
  1. 1.School of Computing and CommunicationsLancaster UniversityLancasterUK
  2. 2.School of Computing and CommunicationsLancaster UniversityLancasterUK

Bibliographic information