Name
Abstract | ||
|---|---|---|
Learning the parameters of graphical models using the maximum likelihood estimation is generally hard which requires an approximation. Maximum composite likelihood estimations are statistical approximations of the maximum likelihood estimation which are higher-order generalizations of the maximum pseudo-likelihood estimation. In this paper, we propose a composite likelihood method and investigate its property. Furthermore, we apply our composite likelihood method to restricted Boltzmann machines. |
Year | Venue | Keywords |
|---|---|---|
2012 | international conference on pattern recognition | Boltzmann machines,approximation theory,higher order statistics,maximum likelihood estimation,solid modelling,graphical models,higher order generalization,maximum composite likelihood estimation,maximum pseudolikelihood estimation,restricted Boltzmann machine,statistical approximation |
DocType | Volume | ISSN |
Conference | abs/1406.6176 | Proceedings of 21st International Conference on Pattern
Recognition (ICPR2012), pp. 2234-2237, 2012 |
ISBN | Citations | PageRank |
978-1-4673-2216-4 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Muneki Yasuda | 1 | 9 | 7.79 |
| Shun Kataoka | 2 | 4 | 3.23 |
| Yuji Waizumi | 3 | 37 | 5.86 |
| Kazuyuki Tanaka | 4 | 9 | 4.06 |