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142
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GECCO
2009
Springer
159views Optimization» more  GECCO 2009»
15 years 8 months ago
Bayesian network structure learning using cooperative coevolution
We propose a cooperative-coevolution – Parisian trend – algorithm, IMPEA (Independence Model based Parisian EA), to the problem of Bayesian networks structure estimation. It i...
Olivier Barrière, Evelyne Lutton, Pierre-He...
116
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SIGCSE
1997
ACM
121views Education» more  SIGCSE 1997»
15 years 7 months ago
Application-based modules using apprentice learning for CS 2
A typical Data Structures (CS 2) course covers a wide variety of topics: elementary algorithm analysis; data structures including dynamic structures, trees, tables, graphs, etc.; ...
Owen L. Astrachan, Robert F. Smith, James T. Wilke...
TCBB
2011
14 years 10 months ago
Learning Genetic Regulatory Network Connectivity from Time Series Data
Recent experimental advances facilitate the collection of time series data that indicate which genes in a cell are expressed. This paper proposes an efficient method to generate th...
Nathan A. Barker, Chris J. Myers, Hiroyuki Kuwahar...
ESANN
2007
15 years 4 months ago
Causality and communities in neural networks
A recently proposed nonlinear extension of Granger causality is used to map the dynamics of a neural population onto a graph, whose community structure characterizes the collective...
Leonardo Angelini, Daniele Marinazzo, Mario Pellic...
IPL
2008
172views more  IPL 2008»
15 years 3 months ago
Approximation algorithms for restricted Bayesian network structures
Bayesian Network structures with a maximum in-degree of k can be approximated with respect to a positive scoring metric up to an factor of 1/k. Key words: approximation algorithm,...
Valentin Ziegler