Supervised learning deals with the problem of discovering models from data as relationships between input and output attributes. Two types of models are distinguished: regression models (for continuous output attributes) and classification models (for discrete output attributes). This thesis addresses both regression and classification problems with an emphasis on new applications and on presenting improved evolutionary techniques. Such techniques include Gene Expression Programming (classical and its adaptive version), Genetic Programming, and the hypernetwork model of learning (classical and its evolutionary version). Such methods can be successfully applied to many problems from various domains. This thesis presents applications for symbolic regression for inverse problems, quantum circuit design, modeling of dynamic processes, and forecasting price movement.
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Zustand: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Bautu ElenaElena Bautu received her Ph.D. degree in Artificial Intelligence in 2010 from the Alexandru Ioan Cuza University (Romania). Her doctoral studies covered the application of nature inspired meta-heuristics and learning mod. Bestandsnummer des Verkäufers 5521926
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Taschenbuch. Zustand: Neu. Intelligent Techniques for Data Modeling Problems | Nature inspired meta-heuristics and learning models applied to time series modeling and forecasting | Elena B utu | Taschenbuch | Englisch | LAP Lambert Academic Publishing | EAN 9783848434794 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. Bestandsnummer des Verkäufers 106577143
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Taschenbuch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Supervised learning deals with the problem of discovering models from data as relationships between input and output attributes. Two types of models are distinguished: regression models (for continuous output attributes) and classification models (for discrete output attributes). This thesis addresses both regression and classification problems with an emphasis on new applications and on presenting improved evolutionary techniques. Such techniques include Gene Expression Programming (classical and its adaptive version), Genetic Programming, and the hypernetwork model of learning (classical and its evolutionary version). Such methods can be successfully applied to many problems from various domains. This thesis presents applications for symbolic regression for inverse problems, quantum circuit design, modeling of dynamic processes, and forecasting price movement. Bestandsnummer des Verkäufers 9783848434794
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