Hierarchical Voting Experts: An Unsupervised Algorithm for Hierarchical Sequence Segmentation

被引：3

作者：

Miller, Matthew ^{[1
]}

Stoytchev, Alexander ^{[1
]}

机构：

[1] Iowa State Univ, Dev Robot Lab, Ames, IA 50011 USA

来源：

2008 IEEE 7TH INTERNATIONAL CONFERENCE ON DEVELOPMENT AND LEARNING | 2008年

关键词：

D O I：

10.1109/DEVLRN.2008.4640827

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper extends the Voting Experts (VE) algorithm for unupervised 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.

引用

页码：186 / 191

页数：6

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