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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/8046

Title: On Phase Transitions in Learning Sparse Networks
Authors: HOLLANDERS, Goele
BEX, Geert Jan
WESTRA, Ronald
Issue Date: 2007
Citation: DASTANI, Mohammad Mehdi & DE JONG, Edwin (Ed.) Proceedings of the 19th Belgian-Dutch Conference on Artificial Intelligence, November 2007, Utrecht, The Netherlands. p. 359-360.
Abstract: In this paper [1] we study the identification of sparse interaction networks, from a given set of observations, as a machine learning problem. An example of such a network is a sparse gene-protein interaction network, for more details see [2]. 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 student-teacher setting in machine learning.
URI: http://hdl.handle.net/1942/8046
Category: C2
Type: Proceedings Paper
Appears in Collections: Research publications

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