727 search results - page 53 / 146 » On Bayesian model and variable selection using MCMC |
119
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ESANN
2006
14 years 11 months ago
2006
Random Forests were introduced by Breiman for feature (variable) selection and improved predictions for decision tree models. The resulting model is often superior to AdaBoost and ...
IDEAL
2000
Springer
15 years 1 months ago
2000
Springer
Quantization of continuous variables is important in data analysis, especially for some model classes such as Bayesian networks and decision trees, which use discrete variables. Of...
144
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DAGM
2006
Springer
15 years 1 months ago
2006
Springer
We present a new approach to integrated motion estimation and segmentation by combining methods from discrete and continuous optimization. The velocity of each of a set of regions ...
CSDA
2010
14 years 10 months ago
2010
A two component parametric mixture is proposed to model survival after an invasive treatment, when patients may experience different hazards regimes: a risk of early mortality dir...
ICMLA
2009
14 years 7 months ago
2009
Abstract--Feature selection is an important challenge in machine learning. Unfortunately, most methods for automating feature selection are designed for supervised learning tasks a...