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http://hdl.handle.net/1942/7803
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DC Field | Value | Language |
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dc.contributor.author | HOLLANDERS, Goele | - |
dc.contributor.author | BEX, Geert Jan | - |
dc.contributor.author | GYSSENS, Marc | - |
dc.contributor.author | WESTRA, Ronald | - |
dc.contributor.author | TUYLS, Karl | - |
dc.date.accessioned | 2008-02-04T15:32:44Z | - |
dc.date.available | 2008-02-04T15:32:44Z | - |
dc.date.issued | 2007 | - |
dc.identifier.citation | MACHINE LEARNING: ECML 2007, PROCEEDINGS, 4701. p. 591-599 | - |
dc.identifier.isbn | 978-3-540-74957-8 | - |
dc.identifier.issn | 0302-9743 | - |
dc.identifier.uri | http://hdl.handle.net/1942/7803 | - |
dc.description.abstract | 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 student-teacher setting in machine learning. In the first part of this paper we introduce a learning algorithm, based on L-1-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 very good fit. | - |
dc.format.extent | 197130 bytes | - |
dc.format.mimetype | application/pdf | - |
dc.language.iso | en | - |
dc.publisher | SPRINGER-VERLAG BERLIN | - |
dc.relation.ispartofseries | LECTURE NOTES IN COMPUTER SCIENCE | - |
dc.subject.other | machine learning, sparse network reconstruction, feature identification | - |
dc.title | On phase transitions in learning sparse networks | - |
dc.type | Journal Contribution | - |
local.bibliographicCitation.authors | Kok, JN Koronacki, J DeMantaras, RL Matwin, S Mladenic, D Skowron, A | - |
local.bibliographicCitation.conferencename | 18th European Conference on Machine Learning (ECML 2007)/11th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD 2007) | - |
dc.identifier.epage | 599 | - |
dc.identifier.spage | 591 | - |
dc.identifier.volume | 4701 | - |
local.format.pages | 9 | - |
local.bibliographicCitation.jcat | A1 | - |
dc.description.notes | Hasselt Univ, Dept Math Phys & Comp Sci, Hasselt, Belgium.Hollanders, G, Hasselt Univ, Dept Math Phys & Comp Sci, Hasselt, Belgium. | - |
local.type.refereed | Refereed | - |
local.type.specified | Article | - |
local.relation.ispartofseriesnr | 4701 | - |
dc.bibliographicCitation.oldjcat | C1 | - |
dc.identifier.doi | 10.1007/978-3-540-74958-5_57 | - |
dc.identifier.isi | 000249742300053 | - |
item.accessRights | Open Access | - |
item.fullcitation | HOLLANDERS, Goele; BEX, Geert Jan; GYSSENS, Marc; WESTRA, Ronald & TUYLS, Karl (2007) On phase transitions in learning sparse networks. In: MACHINE LEARNING: ECML 2007, PROCEEDINGS, 4701. p. 591-599. | - |
item.contributor | HOLLANDERS, Goele | - |
item.contributor | BEX, Geert Jan | - |
item.contributor | GYSSENS, Marc | - |
item.contributor | WESTRA, Ronald | - |
item.contributor | TUYLS, Karl | - |
item.fulltext | With Fulltext | - |
item.validation | ecoom 2008 | - |
crisitem.journal.issn | 0302-9743 | - |
Appears in Collections: | Research publications |
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File | Description | Size | Format | |
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ECML2007.pdf | 192.51 kB | Adobe PDF | View/Open |
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