
Empirical Approach to Machine Learning - Studies in Computational Intelligence 800 (Paperback)
Plamen P. Angelov (author), Xiaowei Gu (author)
£139.99
Paperback
423 Pages /
Published: 10/12/2019
- Temporarily unavailable
Email me when available
Stay one step ahead and let us notify you when this item is next available to order
Email me when available
Enter your email below and we will notify you when this item is next available to order.

Empirical Approach to Machine Learning - Studies in Computational Intelligence 800 (Paperback)
£139.99
Thank you
We will contact you when this item is next available to order.
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, 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."
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."
Publisher: Springer Nature Switzerland AG
ISBN: 9783030132095
Number of pages: 423
Weight: 694 g
Dimensions: 235 x 155 mm
Edition: Softcover reprint of the original 1st ed. 201
You may also be interested in...
Please sign in to write a review
Sign In / Register
Not registered? CREATE AN ACCOUNTCREATE A plus ACCOUNT
Sign In
Download the Waterstones App
Would you like to proceed to the App store to download the Waterstones App?
Click & Collect
Reserve online, pay on collection
Alternatively, for multiple items you may find it easier to add to basket, then pay online and collect in as little as 2 hours, subject to availability.
Alternatively, for multiple items you may find it easier to add to basket, then pay online and collect in as little as 2 hours, subject to availability.
Thank you for your reservation
Your order is now being processed and we have sent a confirmation email to you at
When will my order be ready to collect?
Following the initial email, you will be contacted by the shop to confirm that your item is available for collection.
Call us on or send us an email at
Unfortunately there has been a problem with your order
Please try again or alternatively you can contact your chosen shop on or send us an email at