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ECML
2007
Springer

On Phase Transitions in Learning Sparse Networks

13 years 9 months ago
On Phase Transitions in Learning Sparse Networks
In this paper we study the identification of sparse interaction networks as a machine learning problem. Sparsity means that we are provided with a small data set and a high number of unknown components of the system, most of which are zero. Under these circumstances, a model needs to be learned that fits the underlying system, capable of generalization. This corresponds to the studentteacher setting in machine learning. In the first part of this paper we introduce a learning algorithm, based on L1-minimization, to identify interaction networks from poor data and analyze its dynamics with respect to phase transitions. The efficiency of the algorithm is measured by the generalization error, which represents the probability that the student is a good fit to the teacher. In the second part of this paper we show that from a system with a specific system size value the generalization error of other system sizes can be estimated. A comparison with a set of simulation experiments show a v...
Goele Hollanders, Geert Jan Bex, Marc Gyssens, Ron
Added 07 Jun 2010
Updated 07 Jun 2010
Type Conference
Year 2007
Where ECML
Authors Goele Hollanders, Geert Jan Bex, Marc Gyssens, Ronald L. Westra, Karl Tuyls
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