Name
Abstract | ||
|---|---|---|
We present a new statistical learning paradigm for Boltzmann machines based on a new inference principle we have proposed: the latent maximum entropy principle (LME). LME is different both from Jaynes' maximum entropy principle and from standard maximum likelihood estimation. We demonstrate the LME principle by deriving new algorithms for Boltzmann machine parameter estimation, and show how a robust and rapidly convergent new variant of the EM algorithm can be developed. Our experiments show that estimation based on LME generally yields better results than maximum likelihood estimation when inferring models from small amounts of data. |
Year | Venue | Keywords |
|---|---|---|
2012 | UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence | boltzmann machine parameter estimation,maximum likelihood estimation,new statistical learning paradigm,maximum entropy principle,new inference principle,latent maximum entropy principle,standard maximum likelihood estimation,new algorithm,lme principle,convergent new variant,boltzmann machine |
DocType | Volume | ISBN |
Journal | abs/1212.2514 | 0-127-05664-5 |
Citations | PageRank | References |
3 | 0.45 | 9 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shaojun Wang | 1 | 468 | 38.96 |
| Dale Schuurmans | 2 | 2760 | 317.49 |
| Fuchun Peng | 3 | 1378 | 85.75 |
| Yunxin Zhao | 4 | 807 | 121.74 |