4 search results - page 1 / 1 » Occam factors and model-independent Bayesian learning of con... |
CORR
2000
Springer
13 years 7 months ago
2000
Springer
NIPS
2000
13 years 8 months ago
2000
Learning of a smooth but nonparametric probability density can be regularized using methods of Quantum Field Theory. We implement a field theoretic prior numerically, test its eff...
GECCO
2004
Springer
14 years 25 days ago
2004
Springer
This paper describes a continuous estimation of distribution algorithm (EDA) to solve decomposable, real-valued optimization problems quickly, accurately, and reliably. This is the...
ESOP
2011
Springer
12 years 11 months ago
2011
Springer
Abstract. The Bayesian approach to machine learning amounts to inferring posterior distributions of random variables from a probabilistic model of how the variables are related (th...