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National Repository of Grey Literature 2 records found  Search took 0.01 seconds. 
Dynamic Bayesian Networks for the Classification of Sleep Stages
 Vomlel, Jiří ; Kratochvíl, Václav
Human sleep is traditionally classified into five (or six) stages. The manual classification is time consuming since it requires knowledge of an extensive set of rules from manuals and experienced experts. Therefore automatic classification methods appear useful for this task. In this paper we extend the approach based on Hidden Markov Models by relating certain features not only to the current time slice but also to the previous one. Dynamic Bayesian Networks that results from this generalization are thus capable of modeling features related to state transitions. Experiments on real data revealed that in this way we are able to increase the prediction accuracy.
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User Friendly Envioronment for Dynamic Bayesian Networks
 Vinárek, Jan ; Kadlec, Rudolf (advisor) ; Skřivánek, Zdeněk (referee)
Title: User Friendly Environment for Dynamic Bayesian Networks Author: Jan Vinárek Department: Department of Software and Computer Science Education Supervisor: Mgr. Rudolf Kadlec, Department of Software and Computer Science Education Abstract: For open source tools with the graphical interface which are focused on datamining and written in the Java language there is a small support for processing of sequential data. One of the most popular models used for processing of sequential data is the dynamic Bayesian network, with the use of its inference algorithms. The aim of the theoretical part of the thesis was to find a program which supports graphical interface for datamining with a simple control and library which imple- ments inference algorithms of dynamic Bayesian networks in the best way. The aim of the practical part was to design and to program the extension for the chosen program (RapidMiner) with the use of the found library (JSMILE). In the ex- tension the combination of uses of learning algorithm Expectation-Maximization and inference algorithm of dynamic Bayesian network was tested for prediction of sequential data. The combination was compared to the use of learning models Support Vector Machines and Decision Tree on two examples. Keywords: dynamic Bayesian network, sequential data, time series, Java
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