A method to add Hidden Markov Models with application to learning articulated motion | Zendy

Yulia Hicks | Zendy; Peter Hall | Zendy; D. Marshall | Zendy

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A method to add Hidden Markov Models with application to learning articulated motion

Author(s) -

Yulia Hicks,

Peter Hall,

D. Marshall

Publication year - 2003

Publication title -

citeseer x (the pennsylvania state university)

Language(s) - English

Resource type - Conference proceedings

DOI - 10.5244/c.17.50

Subject(s) - hidden markov model , computer science , independence (probability theory) , artificial intelligence , machine learning , markov model , motion (physics) , markov chain , markov process , maximum entropy markov model , data modeling , training set , pattern recognition (psychology) , variable order markov model , mathematics , statistics , database

In this paper we present a method for adding Hidden Markov Models. The main advantages of our method are that it does not require the data the models had been trained on, allows a change in the number of components, does not assume independence of the components to be added and is resistant to the order in which the training data arrives. We assessed the method in the experiments with synthetic data, which showed good accuracy. Finally, we present an application in computer vision.

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