Learning and Using Multimodal Stochastic Models : A Unified Approach
|Other Titles:||Erlernen und Anwendung von multimodalen stochastischen Modellen : ein einheitlicher Ansatz||Authors:||Edgington, Mark||Supervisor:||Kirchner, Frank||1. Expert:||Kirchner, Frank||2. Expert:||Beetz, Michael, Ph.D.||Abstract:||
This dissertation presents a principled approach to representing and using instance-based knowledge. Perceptions and actions are probabilistically modelled in a unified structure which allows for simultaneous perception modelling and reasoning about desired actions. In particular, a new method for online instance-based learning of such models is presented and analyzed. This method, called Dynamic Gaussian Mixture Estimation (DGME), adapts a model's complexity to the process being modelled. The models produced by DGME are evaluated on several classification, prediction, and control applications, and its characteristics are compared with other state-of-the-art methods. In the context of control applications, an additional novel method, Gaussian Mixture Control (GMC), is introduced for precisely controlling systems that exhibit multimodality.
|Keywords:||Density Estimation, Online Learning, Bayesian Learning, Probabilistic Inference, Mixture Model, Cognitive Modeling, Robot Behavior and Control||Issue Date:||9-Sep-2016||URN:||urn:nbn:de:gbv:46-00105528-16||Institution:||Universität Bremen||Faculty:||FB3 Mathematik/Informatik|
|Appears in Collections:||Dissertationen|
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