Name
Abstract | ||
|---|---|---|
This letter proposes a new algorithm for Gaussian process classification based on posterior linearization (PL). In PL, a Gaussian approximation to the posterior density is obtained iteratively using the best possible linearization of the conditional mean of the labels and accounting for the linearization error. PL has some theoretical advantages over expectation propagation (EP): all calculated co... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/LSP.2019.2906929 | IEEE Signal Processing Letters |
Keywords | Field | DocType |
Covariance matrices,Mean square error methods,Approximation algorithms,Gaussian approximation,Signal processing algorithms,Convergence,Gaussian processes | Bayesian inference,Pattern recognition,Efficient energy use,Algorithm,Artificial intelligence,Gaussian process,Mathematics,Linearization | Journal |
Volume | Issue | ISSN |
26 | 5 | 1070-9908 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Angel F. Garcia-Fernandez | 1 | 131 | 18.15 |
| Filip Tronarp | 2 | 8 | 5.65 |
| Simo Särkkä | 3 | 623 | 66.52 |