Name
Abstract | ||
|---|---|---|
Gaussian process (GP) probabilistic models have attractive advantages over parametric and neural network modeling approaches. They have a small number of tuneable parameters, can be trained on relatively small training sets, and provide a measure of prediction certainty. In this paper, these properties are exploited to develop two methods of highlighting the presence of neonatal seizures from elec... |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1109/TBME.2007.895745 | IEEE Transactions on Biomedical Engineering |
Keywords | DocType | Volume |
Gaussian processes,Brain modeling,Electroencephalography,Pediatrics,Neural networks,Predictive models,Testing,Signal processing,Current measurement,Particle measurements | Journal | 54 |
Issue | ISSN | Citations |
12 | 0018-9294 | 16 |
PageRank | References | Authors |
1.24 | 6 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Stephen Faul | 1 | 148 | 8.21 |
| Gregor Gregorcic | 2 | 81 | 5.51 |
| Geraldine Boylan | 3 | 45 | 4.60 |
| William Marnane | 4 | 145 | 11.11 |
| Gordon Lightbody | 5 | 223 | 27.57 |
| Sean Connolly | 6 | 30 | 4.81 |