DocumentCode
3032671
Title
Hierarchical voting experts: An unsupervised algorithm for hierarchical sequence segmentation
Author
Miller, Matthew ; Stoytchev, Alexander
Author_Institution
Dev. Robot. Lab., Iowa State Univ., Ames, IA
fYear
2008
fDate
9-12 Aug. 2008
Firstpage
186
Lastpage
191
Abstract
This paper extends the voting experts (VE) algorithm for unsupervised segmentation of sequences to create the hierarchical voting experts (HVE) algorithm for unsupervised segmentation of hierarchically structured sequences. The paper evaluates the strengths and weaknesses of the HVE algorithm to identify its proper domain of application. The paper also shows how higher order models of the sequence data can be used to improve lower level segmentation accuracy.
Keywords
artificial intelligence; biocybernetics; hierarchical systems; information theory; pattern recognition; HVE algorithm; hierarchical sequence segmentation; hierarchical voting experts algorithm; hierarchically structured sequences; sequence data high order models; unsupervised segmentation algorithm; Clustering algorithms; Entropy; Humans; Probability; Robots; Shape; Speech; Statistical learning; Streaming media; Voting;
fLanguage
English
Publisher
ieee
Conference_Titel
Development and Learning, 2008. ICDL 2008. 7th IEEE International Conference on
Conference_Location
Monterey, CA
Print_ISBN
978-1-4244-2661-4
Electronic_ISBN
978-1-4244-2662-1
Type
conf
DOI
10.1109/DEVLRN.2008.4640827
Filename
4640827
Link To Document