175 search results - page 17 / 35 » Learning Continuous Time Bayesian Networks |
IDEAL
2005
Springer
14 years 1 months ago
2005
Springer
Abstract. A novel Genetic Programming (GP) paradigm called Coevolutionary Rule-Chaining Genetic Programming (CRGP) has been proposed to learn the relationships among attributes rep...
ICANNGA
2009
Springer
14 years 2 months ago
2009
Springer
We propose a generalized model with configurable discretizer actuators as a solution to the problem of the discretization of massive numerical datasets. Our solution is based on a ...
AI
2010
Springer
13 years 7 months ago
2010
Springer
Bayesian networks (BNs) are used to represent and ef ciently compute with multi-variate probability distributions in a wide range of disciplines. One of the main approaches to per...
ECCV
2006
Springer
14 years 9 months ago
2006
Springer
Abstract. There has been growing interest in developing nonlinear dimensionality reduction algorithms for vision applications. Although progress has been made in recent years, conv...
JMLR
2010
13 years 2 months ago
2010
Deep belief networks are a powerful way to model complex probability distributions. However, it is difficult to learn the structure of a belief network, particularly one with hidd...