Abstract
A method for Gaussian process learning of a scalar function from a set of pair-wise order relationships is presented. Expectation propagation is used to obtain an approximation to the log marginal likelihood which is optimised using an analytical expression for its gradient. Experimental results show that the proposed method performs well compared with a previous method for Gaussian process preference learning.
| Original language | English |
|---|---|
| Title of host publication | Proceedings - International Conference on Pattern Recognition |
| Pages | 605-608 |
| Number of pages | 4 |
| DOIs | |
| Publication status | Published - 2010 |
| Event | 20th International Conference on Pattern Recognition - Istanbul, Turkey Duration: 23 Aug 2010 → 26 Aug 2010 |
Conference
| Conference | 20th International Conference on Pattern Recognition |
|---|---|
| Abbreviated title | ICPR 2010 |
| Country | Turkey |
| City | Istanbul |
| Period | 23/08/10 → 26/08/10 |
Keywords
- Analytical expressions
- Expectation propagation
- Gaussian processes
- Marginal likelihood
- Preference learning
- Scalar function
- Gaussian noise (electronic)
- Pattern recognition
- Gaussian distribution
Fingerprint Dive into the research topics of 'Gaussian process learning from order relationships using expectation propagation'. Together they form a unique fingerprint.
Cite this
Wang, R., & McKenna, S. J. (2010). Gaussian process learning from order relationships using expectation propagation. In Proceedings - International Conference on Pattern Recognition (pp. 605-608) https://doi.org/10.1109/ICPR.2010.153