Xiaowei gu (20 Ergebnisse)

Hardcover. Zustand: Fine. Zustand des Schutzumschlags: Very Good. Unread. No markings in book. Binding is tight. Dual language, English and Chinese. DJ has light shelf wear.

Zustand: New.

Zustand: As New. Unread book in perfect condition.

Zustand: New.

Zustand: As New. Unread book in perfect condition.

Zustand: New.

Zustand: New.

Zustand: New. pp. 423.

Zustand: New. 1st ed. 2019 edition NO-PA16APR2015-KAP.

Paperback. Zustand: Brand New. reprint edition. 456 pages. 9.25x6.10x0.98 inches. In Stock.

Hardcover. Zustand: Brand New. 456 pages. 9.25x6.10x1.10 inches. In Stock.

Black and white photographic illustrations, 168pp, dustjacket rubbed, a very good hardback copy. Text in English and Chinese, quarto.

Gebunden. Zustand: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. New efficient methods for pattern recognition and machine learning in data-rich environmentsFocuses on automated methods, which can be easily adapted to various applicationsCovers techniques with high level of autonomy, capable to deal with.…

Zustand: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. New efficient methods for pattern recognition and machine learning in data-rich environmentsFocuses on automated methods, which can be easily adapted to various applicationsCovers techniques with high level of autonomy, capable to deal with.…

Taschenbuch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. Itcan 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.' 456 pp. Englisch.…

Buch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. Itcan 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.' 456 pp. Englisch.…

Zustand: New. Print on Demand pp. 423.

Zustand: New. Print on Demand.

Zustand: New. PRINT ON DEMAND pp. 423.

Zustand: New. PRINT ON DEMAND.