ISBN: 9781799883500 - implementation of machine learning algorithms using control-flow and dataflow paradigms (9 risultati)

Condizione: New.

Condizione: New. In English.

Hardback. Condizione: New.

Hardback. Condizione: New.

HRD. Condizione: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

HRD. Condizione: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Serves as reference for professionals who would like to advance their research of energy efficient accelerators for machine learning algorithms like data mining, and to switch from the existing control-flow paradigm to energy efficient dataflow paradigm.…

Buch. Condizione: Neu. Implementation of Machine Learning Algorithms Using Control-Flow and Dataflow Paradigms | Veljko Milutinovi¿ (u. a.) | Buch | Gebunden | Englisch | 2022 | Engineering Science Reference | EAN 9781799883500 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.…

Buch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Based on current literature and cutting-edge advances in the machine learning field, there are four algorithms whose usage in new application domains must be explored: neural networks, rule induction algorithms, tree-based algorithms, and density-based algorithms. A number of machine learning related algorithms have been derived from these four algorithms. Consequently, they represent excellent underlying methods for extracting hidden knowledge from unstructured data, as essential data mining tasks. Implementation of Machine Learning Algorithms Using Control-Flow and Dataflow Paradigms presents widely used data-mining algorithms and explains their advantages and disadvantages, their mathematical treatment, applications, energy efficient implementations, and more. It presents research of energy efficient accelerators for machine learning algorithms. Covering topics such as control-flow implementation, approximate computing, and decision tree algorithms, this book is an essential resource for computer scientists, engineers, students and educators of higher education, researchers, and academicians. …