Name
Abstract | ||
|---|---|---|
Time-series segmentation in the fully unsupervised scenario in which the number of segment-types is a priori unknown is a fundamental problem in many applications. We propose a Bayesian approach to a segmentation model based on the switching linear Gaussian ... |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/ICMLA.2008.50 | ICMLA |
Keywords | Field | DocType |
parallel som neural network,time-series segmentation,bayesian approach,unsupervised scenario,linear gaussian,fundamental problem,segmentation model,region growing,clustering,couplings,classification algorithms,clustering algorithms,self organizing map,artificial neural networks,data mining,neural network | Cluster (physics),Data mining,Computer science,Self-organizing map,Region growing,Artificial intelligence,Artificial neural network,Cluster analysis,Pattern recognition,Statistical classification,Machine learning,Pattern identification,Grid | Conference |
Citations | PageRank | References |
5 | 0.49 | 6 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Iren Valova | 1 | 136 | 25.44 |
| Daniel MacLean | 2 | 113 | 11.82 |
| Derek Beaton | 3 | 47 | 5.52 |