عنوان

Empirical approach to machine learning /

(Number (ISBN

3030023834

(Number (ISBN

3030023842

(Number (ISBN

3030023850

(Number (ISBN

3030132099

(Number (ISBN

9783030023836

(Number (ISBN

9783030023843

(Number (ISBN

9783030023850

(Number (ISBN

9783030132095

Erroneous ISBN

9783030023836

Title Proper

Empirical approach to machine learning /

General Material Designation

[Book]

First Statement of Responsibility

Plamen P. Angelov, Xiaowei Gu.

Place of Publication, Distribution, etc.

Cham, Switzerland :

Name of Publisher, Distributor, etc.

Springer,

Date of Publication, Distribution, etc.

[2019]

Specific Material Designation and Extent of Item

1 online resource (xxix, 423 pages) :

Other Physical Details

illustrations (some color)

Series Title

Studies in Computational Intelligence,

Volume Designation

800

ISSN of Series

1860-949X ;

Text of Note

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.

0

Text of Note

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."

International Standard Book Number

9783030023836

International Standard Book Number

9783030023850

Machine learning.

Big data.

Data mining.

Engineering.

Optical pattern recognition.

COM004000

UYQ

UYQ

Number

Edition

Class number

Book number

Angelov, Plamen P.

Gu, Xiaowei.

Date of Transaction

20200823074137.0

Cataloguing Rules (Descriptive Conventions))

rda

Electronic name

[Book]

Y