Name
Abstract | ||
|---|---|---|
Hierarchical hidden Markov models (HHMMs) can be used for time series segmentation. However, it is difficult to obtain a desirable segmentation result, because the form of learning for HHMMs is unsupervised. In the paper, we present a semisupervised learning algorithm for HHMMs. It is semisupervised in the sense that the supervisor teaches segmentation boundaries but not segment labels. The learning performance of the proposed algorithm is demonstrated through an experiment using music data. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1007/11551188_59 | ICAPR (1) |
Keywords | Field | DocType |
hierarchical hmms,segment label,hierarchical hidden markov model,desirable segmentation result,segment boundary,semisupervised learning algorithm,music data,time series segmentation,proposed algorithm,segmentation boundary,time series | Supervisor,Time-series segmentation,Markov model,Segmentation,Computer science,Unsupervised learning,Artificial intelligence,Hidden Markov model,Machine learning | Conference |
Volume | ISSN | ISBN |
3686 | 0302-9743 | 3-540-28757-4 |
Citations | PageRank | References |
2 | 0.39 | 2 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Naoto Gotou | 1 | 2 | 0.39 |
| Akira Hayashi | 2 | 51 | 9.08 |
| Nobuo Suematu | 3 | 2 | 0.39 |