38 search results - page 2 / 8 » Parametric Learning and Monte Carlo Optimization |
ECAI
2010
Springer
13 years 6 months ago
2010
Springer
In the last decades enormous advances have been made possible for modelling complex (physical) systems by mathematical equations and computer algorithms. To deal with very long run...
DAC
2010
ACM
13 years 5 months ago
2010
ACM
With technology scaling down to 90nm and below, many yield-driven design and optimization methodologies have been proposed to cope with the prominent process variation and to incr...
ICCV
2001
IEEE
14 years 7 months ago
2001
IEEE
?This paper presents a computational paradigm called Data-Driven Markov Chain Monte Carlo (DDMCMC) for image segmentation in the Bayesian statistical framework. The paper contribut...
ML
2007
ACM
13 years 5 months ago
2007
ACM
We propose a general-purpose stochastic optimization algorithm, the so-called annealing stochastic approximation Monte Carlo (ASAMC) algorithm, for neural network training. ASAMC c...
IDEAL
2007
Springer
13 years 11 months ago
2007
Springer
The dependence of the classification error on the size of a bagging ensemble can be modeled within the framework of Monte Carlo theory for ensemble learning. These error curves ar...