analysis data driven modelling de christopher a b arockia (4 resultados)

Taschenbuch. Condición: 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.

Condición: 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 .

Taschenbuch. Condición: 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.

Taschenbuch. Condición: 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.