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.2008.95 | ICMLA |
Keywords | Field | DocType |
time-series segmentation,general regression neural networks,bayesian approach,unsupervised scenario,linear gaussian,fundamental problem,segmentation model,automating microarray classification,accuracy,colon cancer,genetics,prediction algorithms,classification algorithms,receiver operating characteristics curve,regression analysis,receiver operating characteristic curve,learning artificial intelligence,cancer,artificial neural networks,machine learning | Data mining,General regression neural network,Receiver operating characteristic,Computer science,Regression analysis,Artificial intelligence,Artificial neural network,Microarray,Regression,Pattern recognition,Statistical classification,Machine learning,Particle swarm optimizer | Conference |
Citations | PageRank | References |
3 | 0.38 | 19 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Caio Soares | 1 | 11 | 2.61 |
| Lacey Montgomery | 2 | 10 | 1.60 |
| Kenneth Rouse | 3 | 3 | 0.38 |
| Juan E. Gilbert | 4 | 170 | 44.51 |