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» Learning Bayesian Networks with Local Structure
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AI
2010
Springer
15 years 2 months ago
Understanding the scalability of Bayesian network inference using clique tree growth curves
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...
Ole J. Mengshoel
NIPS
1996
15 years 3 months ago
Solving the Ill-Conditioning in Neural Network Learning
Abstract. In this paper we investigate the feed-forward learning problem. The well-known ill-conditioning which is present in most feed-forward learning problems is shown to be the...
P. Patrick van der Smagt, Gerd Hirzinger
BMCBI
2011
14 years 5 months ago
A hierarchical Bayesian network approach for linkage disequilibrium modeling and data-dimensionality reduction prior to genome-w
Background: Discovering the genetic basis of common genetic diseases in the human genome represents a public health issue. However, the dimensionality of the genetic data (up to 1...
Raphael Mourad, Christine Sinoquet, Philippe Leray
GECCO
2006
Springer
195views Optimization» more  GECCO 2006»
15 years 5 months ago
Studying XCS/BOA learning in Boolean functions: structure encoding and random Boolean functions
Recently, studies with the XCS classifier system on Boolean functions have shown that in certain types of functions simple crossover operators can lead to disruption and, conseque...
Martin V. Butz, Martin Pelikan
FOSSACS
2005
Springer
15 years 7 months ago
Branching Cells as Local States for Event Structures and Nets: Probabilistic Applications
We study the concept of choice for true concurrency models such as prime event structures and safe Petri nets. We propose a dynamic variation of the notion of cluster previously in...
Samy Abbes, Albert Benveniste