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
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;
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
DOI :
10.1109/DEVLRN.2008.4640827
