Elena B utu

Intelligent Techniques for Data Modeling Problems

Nature inspired meta-heuristics and learning models applied to time series modeling and forecasting

• Broschiertes Buch

Produktbeschreibung

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.

Produktdetails

• Verlag: LAP Lambert Academic Publishing
• Aufl.
• Seitenzahl: 224
• Englisch
• Abmessung: 220mm
• ISBN-13: 9783848434794
• ISBN-10: 3848434792
• Artikelnr.: 35311215

Autorenporträt

Elena B utu 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 models to data mining problems, focusing on time series modeling and forecasting problems.