4 search results - page 1 / 1 » Occam factors and model-independent Bayesian learning of con... |
CORR
2000
Springer
13 years 4 months ago
2000
Springer
NIPS
2000
13 years 6 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
13 years 10 months 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 8 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...