1405 search results - page 147 / 281 » Learning Instance-Specific Predictive Models |
ESSMAC
2003
Springer
14 years 2 months ago
2003
Springer
Gaussian Process prior models, as used in Bayesian non-parametric statistical models methodology are applied to implement a nonlinear adaptive control law. The expected value of a...
ACIIDS
2010
IEEE
13 years 11 months ago
2010
IEEE
Abstract. A genetic algorithm is used to learn a non-deterministic Petri netbased model of non-linear gene interactions, or statistical epistasis. Petri nets are computational mode...
ICML
2008
IEEE
14 years 9 months ago
2008
IEEE
The infinite hidden Markov model is a nonparametric extension of the widely used hidden Markov model. Our paper introduces a new inference algorithm for the infinite Hidden Markov...
ICML
2010
IEEE
13 years 10 months ago
2010
IEEE
The principle of maximum entropy provides a powerful framework for statistical models of joint, conditional, and marginal distributions. However, there are many important distribu...
ICML
2008
IEEE
14 years 9 months ago
2008
IEEE
Stationarity is often an unrealistic prior assumption for Gaussian process regression. One solution is to predefine an explicit nonstationary covariance function, but such covaria...