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Hierarchical hidden Markov models with general state hierarchy

Bui, Hung H., Phung, Dinh Q. and Venkatesh, Svetha 2004, Hierarchical hidden Markov models with general state hierarchy, in Proceedings of the National Conference on Artificial Intelligence, [American Association for Artificial Intelligence], [San Jose, Calif.], pp. 324-329.

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Title Hierarchical hidden Markov models with general state hierarchy
Author(s) Bui, Hung H.
Phung, Dinh Q.ORCID iD for Phung, Dinh Q. orcid.org/0000-0002-9977-8247
Venkatesh, SvethaORCID iD for Venkatesh, Svetha orcid.org/0000-0001-8675-6631
Conference name Nineteenth National Conference on Artificial Intelligence : Sixteenth Innovative Applications of Artificial Intelligence Conference (2004 : San Jose, Calif.)
Conference location San Jose, Calif.
Conference dates 25-29 July 2004
Title of proceedings Proceedings of the National Conference on Artificial Intelligence
Editor(s) [unknown]
Publication date 2004
Conference series National Conference on Artificial Intelligence
Start page 324
End page 329
Total pages 6
Publisher [American Association for Artificial Intelligence]
Place of publication [San Jose, Calif.]
Keyword(s) hierarchical hidden Markov model
state hierarchy
Summary The hierarchical hidden Markov model (HHMM) is an extension of the hidden Markov model to include a hierarchy of the hidden states. This form of hierarchical modeling has been found useful in applications such as handwritten character recognition, behavior recognition, video indexing, and text retrieval. Nevertheless, the state hierarchy in the original HHMM is restricted to a tree structure. This prohibits two different states from having the same child, and thus does not allow for sharing of common substructures in the model. In this paper, we present a general HHMM in which the state hierarchy can be a lattice allowing arbitrary sharing of substructures. Furthermore, we provide a method for numerical scaling to avoid underflow, an important issue in dealing with long observation sequences. We demonstrate the working of our method in a simulated environment where a hierarchical behavioral model is automatically learned and later used for recognition.
Notes Proceedings - Nineteenth National Conference on Artificial Intelligence (AAAI-2004): Sixteenth Innovative Applications of Artificial Intelligence Conference (IAAI-2004);San Jose, Calif.
Language eng
Field of Research 080199 Artificial Intelligence and Image Processing not elsewhere classified
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1.1 Full written paper - refereed
Persistent URL http://hdl.handle.net/10536/DRO/DU:30044635

Document type: Conference Paper
Collection: School of Information Technology
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