Lingua: Inglese
Editore: LAP LAMBERT Academic Publishing, 2021
ISBN 10: 6204199978 ISBN 13: 9786204199979
Da: preigu, Osnabrück, Germania
EUR 36,25
Quantità: 5 disponibili
Aggiungi al carrelloTaschenbuch. Condizione: Neu. Analysis of Data Driven Modelling in Ecosystem Services | Machine Learning | A. B. Arockia Christopher (u. a.) | Taschenbuch | Englisch | 2021 | LAP LAMBERT Academic Publishing | EAN 9786204199979 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.
Lingua: Inglese
Editore: LAP LAMBERT Academic Publishing Aug 2021, 2021
ISBN 10: 6204199978 ISBN 13: 9786204199979
Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germania
EUR 39,90
Quantità: 2 disponibili
Aggiungi al carrelloTaschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Data-driven modelling is the area of hydro informatics undergoing fast development. This book reviews the main concepts and approaches of data-driven modelling, which is based on computational intelligence and machine-learning methods. A brief overview of the main methods - neural networks, fuzzy rule-based systems and genetic algorithms. Recent developments in machine learning have expanded data-driven modelling (DDM) capabilities, allowing artificial intelligence to infer the behavior of a system by computing and exploiting correlations between observed variables within it. Machine learning algorithms may enable the use of increasingly available 'big data' and assist applying ecosystem service models across scales, analyzing and predicting the flows of these services to disaggregated beneficiaries. 52 pp. Englisch.
Lingua: Inglese
Editore: LAP LAMBERT Academic Publishing, 2021
ISBN 10: 6204199978 ISBN 13: 9786204199979
Da: moluna, Greven, Germania
EUR 34,25
Quantità: Più di 20 disponibili
Aggiungi al carrelloCondizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Christopher A.B.ArockiaDr.A.B.Arockia Christopher,AP(SG), IT, Dr.MCET, Pollachi, Coimbatore, TN, India. He received his PhD in Data mining under I&CE from Anna University Chennai, India. He is a member of ISTE. He has published more .
Lingua: Inglese
Editore: LAP LAMBERT Academic Publishing Aug 2021, 2021
ISBN 10: 6204199978 ISBN 13: 9786204199979
Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germania
EUR 39,90
Quantità: 1 disponibili
Aggiungi al carrelloTaschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Data-driven modelling is the area of hydro informatics undergoing fast development. This book reviews the main concepts and approaches of data-driven modelling, which is based on computational intelligence and machine-learning methods. A brief overview of the main methods - neural networks, fuzzy rule-based systems and genetic algorithms. Recent developments in machine learning have expanded data-driven modelling (DDM) capabilities, allowing artificial intelligence to infer the behavior of a system by computing and exploiting correlations between observed variables within it. Machine learning algorithms may enable the use of increasingly available 'big data' and assist applying ecosystem service models across scales, analyzing and predicting the flows of these services to disaggregated beneficiaries.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 52 pp. Englisch.
Lingua: Inglese
Editore: LAP LAMBERT Academic Publishing, 2021
ISBN 10: 6204199978 ISBN 13: 9786204199979
Da: AHA-BUCH GmbH, Einbeck, Germania
EUR 40,89
Quantità: 1 disponibili
Aggiungi al carrelloTaschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Data-driven modelling is the area of hydro informatics undergoing fast development. This book reviews the main concepts and approaches of data-driven modelling, which is based on computational intelligence and machine-learning methods. A brief overview of the main methods - neural networks, fuzzy rule-based systems and genetic algorithms. Recent developments in machine learning have expanded data-driven modelling (DDM) capabilities, allowing artificial intelligence to infer the behavior of a system by computing and exploiting correlations between observed variables within it. Machine learning algorithms may enable the use of increasingly available 'big data' and assist applying ecosystem service models across scales, analyzing and predicting the flows of these services to disaggregated beneficiaries.