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
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...
Would you like to proceed to the App store to download the Waterstones App?
Please note that owing to current COVID-19 restrictions, many of our shops are closed. Find out more by clicking here.