Angelov, Plamen P. Gu, Xiaowei Empirical approach to machine learning 9783030023836

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Автор: Angelov, Plamen P. Gu, Xiaowei
Название: Empirical approach to machine learning
ISBN: 9783030023836
Издательство: Springer
Классификация:

Кибернетика и теория систем

Спектральный анализ, спектрохимия, масс-спектрометрия

Базы данных

Извлечение знаний из данных

Искусственный интеллект

Машинное обучение

Распознавание образов

ISBN-10: 3030023834
Обложка/Формат: Hardcover
Страницы: 423
Вес: 0.90 кг.
Дата издания: 25.10.2018
Серия: Studies in computational intelligence
Язык: English
Издание: 1st ed. 2019
Иллюстрации: 90 illustrations, color; 49 illustrations, black and white; xxvii, 437 p. 139 illus., 90 illus. in color.
Размер: 240 x 172 x 29
Читательская аудитория: Professional & vocational
Ссылка на Издательство: Link
Рейтинг:
Поставляется из: Германии
Описание: 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.”

Дополнительное описание: 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.- Empiri

Empirical approach to machine learning, Angelov, Plamen P. Gu, Xiaowei