Seller: Rarewaves.com USA, London, LONDO, United Kingdom
£ 308.34
Quantity: Over 20 available
Add to basketHardback. Condition: New.
Seller: Rarewaves.com UK, London, United Kingdom
£ 285.59
Quantity: Over 20 available
Add to basketHardback. Condition: New.
Language: English
Published by Engineering Science Reference, 2022
ISBN 10: 1799883515 ISBN 13: 9781799883517
Seller: preigu, Osnabrück, Germany
Taschenbuch. Condition: Neu. Implementation of Machine Learning Algorithms Using Control-Flow and Dataflow Paradigms | Veljko Milutinovi¿ (u. a.) | Taschenbuch | Englisch | 2022 | Engineering Science Reference | EAN 9781799883517 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.
Language: English
Published by Engineering Science Reference, 2022
ISBN 10: 1799883507 ISBN 13: 9781799883500
Seller: moluna, Greven, Germany
Condition: 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.
Language: English
Published by Engineering Science Reference, 2022
ISBN 10: 1799883515 ISBN 13: 9781799883517
Seller: AHA-BUCH GmbH, Einbeck, Germany
Taschenbuch. Condition: 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.
Language: English
Published by Engineering Science Reference, 2022
ISBN 10: 1799883507 ISBN 13: 9781799883500
Seller: preigu, Osnabrück, Germany
Buch. Condition: 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.
Language: English
Published by Engineering Science Reference, 2022
ISBN 10: 1799883507 ISBN 13: 9781799883500
Seller: AHA-BUCH GmbH, Einbeck, Germany
Buch. Condition: 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.