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 
GYSSENS, Marc 
TUYLS, Karl 
WESTRA, Ronald 
Issue Date: 2007
Source: 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.
Document URI: http://hdl.handle.net/1942/8046
Category: C2
Type: Proceedings Paper
Appears in Collections:Research publications

Files in This Item:
File Description SizeFormat 
BNAIC2007.pdfPublished version77.26 kBAdobe PDFView/Open
Show full item record

Page view(s)

46
checked on May 20, 2022

Download(s)

78
checked on May 20, 2022

Google ScholarTM

Check


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.