Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/16186
Title: A maximum margin dynamic model with its application to brain signal analysis
Authors: XU WENJIE
Keywords: Maximum Margin, Hidden Markov Model, brain signal analysis
Issue Date: 13-Jun-2007
Source: XU WENJIE (2007-06-13). A maximum margin dynamic model with its application to brain signal analysis. ScholarBank@NUS Repository.
Abstract: The work in this dissertation was concentrated on the analysis of continuous brain signals with emphasis on our proposed kernel based hidden Markov model. This unified framework is based on incorporating the hidden Markov model (HMM) with maximum margin principle, having considered that the learning algorithms in Hidden Markov Model (HMM) do not adequately address the arbitrary distribution in brain EEG signal. The hidden Markov model was presented to model interactions between the states of signals and a maximum margin principle was used to learn the model. We presented a formulation for the structured maximum margin learning, taking advantage of the Markov random field representation of the conditional distribution. As a nonparametric learning algorithm, our dynamic model has hence no need of prior knowledge of signal distribution. As a generic time series signal analysis tool, KHMM can be applied to other applications.
URI: http://scholarbank.nus.edu.sg/handle/10635/16186
Appears in Collections:Ph.D Theses (Open)

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