149 search results - page 8 / 30 » Maximum Likelihood Learning of Conditional MTE Distributions |
ICML
2007
IEEE
14 years 8 months ago
2007
IEEE
Conditional log-linear models are a commonly used method for structured prediction. Efficient learning of parameters in these models is therefore an important problem. This paper ...
ICML
2003
IEEE
14 years 8 months ago
2003
IEEE
We show a close relationship between the Expectation - Maximization (EM) algorithm and direct optimization algorithms such as gradientbased methods for parameter learning. We iden...
CORR
2010
Springer
13 years 6 months ago
2010
Springer
Gaussian graphical models are of great interest in statistical learning. Because the conditional independencies between different nodes correspond to zero entries in the inverse c...
ICML
2010
IEEE
13 years 8 months ago
2010
IEEE
Generative topic models such as LDA are limited by their inability to utilize nontrivial input features to enhance their performance, and many topic models assume that topic assig...
ICML
2010
IEEE
13 years 8 months ago
2010
IEEE
Maximum a posteriori (MAP) inference in graphical models requires that we maximize the sum of two terms: a data-dependent term, encoding the conditional likelihood of a certain la...