Name
Abstract | ||
|---|---|---|
In this paper we define conditional random fields in reproducing kernel Hilbert spaces and show connections to Gaussian Process classification. More specifically, we prove decomposition results for undirected graphical models and we give constructions for kernels. Finally we present efficient means of solving the optimization problem using reduced rank decompositions and we show how stationarity can be exploited efficiently in the optimization process. |
Year | Venue | Keywords |
|---|---|---|
2012 | UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence | decomposition result,conditional random field,gaussian process classification,undirected graphical model,show connection,optimization problem,exponential family,reproducing kernel hilbert space,reduced rank decomposition,optimization process,efficient mean,gaussian process,graphical model |
DocType | Volume | ISBN |
Journal | abs/1207.4131 | 0-9749039-0-6 |
Citations | PageRank | References |
16 | 2.70 | 10 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| yasemin altun | 1 | 2463 | 150.46 |
| Alexander J. Smola | 2 | 19627 | 1967.09 |
| Thomas Hofmann | 3 | 10064 | 1001.83 |