Sinossi
Simulation models are a widely used tool for modelling systems for which it is hard to obtain real data. However, the simulation models are usually complex and it is not an easy task to induce new knowledge and find relationships and dependencies among different parts of the simulation model. Previous attempts to analyze the outputs from simulation models were mainly focused on speeding up the simulation process. In this monograph we are proposing a methodology for analyzing results of complex simulation models. The methodology combines simulation outputs, background knowledge, and machine learning, to obtain new and interesting knowledge about a certain problem of interest. We apply our methodology to three different simulation models that simulate the co-existence between genetically-modified and conventional crops at different levels. The induced machine learning models provide us with new co-existence knowledge about the positive and negative influences on the co-existence between genetically-modified and conventional crops. The results encourage us to try the same methodology on different types of simulation models and different scientific areas.
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L'autore
After completing her B.Sc. degree in computer science, Aneta Trajanov joined the Department of Knowledge Technologies at Jozef Stefan Institute in Ljubljana, Slovenia, as a research assistant. Her research interests are in analyzing the results of complex ecological simulation models with machine learning, for which she got a PhD degree.
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Machine learning in agroecology
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LAP LAMBERT Academic Publishing Sep 2011, 2011
ISBN 10: 3845471336
ISBN 13: 9783845471334
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Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Simulation models are a widely used tool for modelling systems for which it is hard to obtain real data. However, the simulation models are usually complex and it is not an easy task to induce new knowledge and find relationships and dependencies among different parts of the simulation model. Previous attempts to analyze the outputs from simulation models were mainly focused on speeding up the simulation process. In this monograph we are proposing a methodology for analyzing results of complex simulation models. The methodology combines simulation outputs, background knowledge, and machine learning, to obtain new and interesting knowledge about a certain problem of interest. We apply our methodology to three different simulation models that simulate the co-existence between genetically-modified and conventional crops at different levels. The induced machine learning models provide us with new co-existence knowledge about the positive and negative influences on the co-existence between genetically-modified and conventional crops. The results encourage us to try the same methodology on different types of simulation models and different scientific areas. 140 pp. Englisch. Codice articolo 9783845471334
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Machine learning in agroecology
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LAP LAMBERT Academic Publishing, 2011
ISBN 10: 3845471336
ISBN 13: 9783845471334
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Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Trajanov AnetaAfter completing her B.Sc. degree in computer science, Aneta Trajanov joined the Department of Knowledge Technologies at Jozef Stefan Institute in Ljubljana, Slovenia, as a research assistant. Her research interests are. Codice articolo 5483952
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Machine learning in agroecology
Editore:
LAP LAMBERT Academic Publishing Sep 2011, 2011
ISBN 10: 3845471336
ISBN 13: 9783845471334
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Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germania
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Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Simulation models are a widely used tool for modelling systems for which it is hard to obtain real data. However, the simulation models are usually complex and it is not an easy task to induce new knowledge and find relationships and dependencies among different parts of the simulation model. Previous attempts to analyze the outputs from simulation models were mainly focused on speeding up the simulation process. In this monograph we are proposing a methodology for analyzing results of complex simulation models. The methodology combines simulation outputs, background knowledge, and machine learning, to obtain new and interesting knowledge about a certain problem of interest. We apply our methodology to three different simulation models that simulate the co-existence between genetically-modified and conventional crops at different levels. The induced machine learning models provide us with new co-existence knowledge about the positive and negative influences on the co-existence between genetically-modified and conventional crops. The results encourage us to try the same methodology on different types of simulation models and different scientific areas.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 140 pp. Englisch. Codice articolo 9783845471334
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Machine learning in agroecology : From simulation models to co-existence rules
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LAP LAMBERT Academic Publishing, 2011
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ISBN 13: 9783845471334
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Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Simulation models are a widely used tool for modelling systems for which it is hard to obtain real data. However, the simulation models are usually complex and it is not an easy task to induce new knowledge and find relationships and dependencies among different parts of the simulation model. Previous attempts to analyze the outputs from simulation models were mainly focused on speeding up the simulation process. In this monograph we are proposing a methodology for analyzing results of complex simulation models. The methodology combines simulation outputs, background knowledge, and machine learning, to obtain new and interesting knowledge about a certain problem of interest. We apply our methodology to three different simulation models that simulate the co-existence between genetically-modified and conventional crops at different levels. The induced machine learning models provide us with new co-existence knowledge about the positive and negative influences on the co-existence between genetically-modified and conventional crops. The results encourage us to try the same methodology on different types of simulation models and different scientific areas. Codice articolo 9783845471334
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Machine learning in agroecology | From simulation models to co-existence rules
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LAP LAMBERT Academic Publishing, 2011
ISBN 10: 3845471336
ISBN 13: 9783845471334
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Taschenbuch. Condizione: Neu. Machine learning in agroecology | From simulation models to co-existence rules | Aneta Trajanov | Taschenbuch | 140 S. | Englisch | 2011 | LAP LAMBERT Academic Publishing | EAN 9783845471334 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu. Codice articolo 106795551
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