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» Reconstructing Metabolic Networks Using Interval Analysis
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NN
2010
Springer
225views Neural Networks» more  NN 2010»
13 years 5 months ago
Learning to imitate stochastic time series in a compositional way by chaos
This study shows that a mixture of RNN experts model can acquire the ability to generate sequences that are combination of multiple primitive patterns by means of self-organizing ...
Jun Namikawa, Jun Tani
IDEAL
2000
Springer
13 years 11 months ago
Quantization of Continuous Input Variables for Binary Classification
Quantization of continuous variables is important in data analysis, especially for some model classes such as Bayesian networks and decision trees, which use discrete variables. Of...
Michal Skubacz, Jaakko Hollmén
SDM
2007
SIAM
143views Data Mining» more  SDM 2007»
13 years 9 months ago
Less is More: Compact Matrix Decomposition for Large Sparse Graphs
Given a large sparse graph, how can we find patterns and anomalies? Several important applications can be modeled as large sparse graphs, e.g., network traffic monitoring, resea...
Jimeng Sun, Yinglian Xie, Hui Zhang, Christos Falo...
BMCBI
2010
110views more  BMCBI 2010»
13 years 7 months ago
TimeDelay-ARACNE: Reverse engineering of gene networks from time-course data by an information theoretic approach
Background: One of main aims of Molecular Biology is the gain of knowledge about how molecular components interact each other and to understand gene function regulations. Using mi...
Pietro Zoppoli, Sandro Morganella, Michele Ceccare...
ECIS
2004
13 years 9 months ago
Value-based business modelling for network organizations: lessons learned from the electricity sector
Speed and availability of information, delivered in past years by Internet technologies, made it easier for any company to outsource primary activities, which resulted in unbundli...
Vera Kartseva, Jaap Gordijn, Yao-Hua Tan