Show simple item record

dc.contributor.authorLappas, G.
dc.contributor.authorFrank, R.
dc.contributor.authorAlbrecht, A.
dc.date.accessioned2010-02-18T11:35:14Z
dc.date.available2010-02-18T11:35:14Z
dc.date.issued2006
dc.identifier.citationLappas , G , Frank , R & Albrecht , A 2006 , ' A Computational Study on Circuit Size vs. Circuit Depth ' , International Journal on Artificial Intelligence Tools , vol. 15 , no. 2 , pp. 143-161 . https://doi.org/10.1142/S0218213006002606
dc.identifier.issn0218-2130
dc.identifier.otherPURE: 97430
dc.identifier.otherPURE UUID: 2a8b0381-b40c-4e7b-b63b-b959b8abfb29
dc.identifier.otherdspace: 2299/4291
dc.identifier.otherScopus: 33746225528
dc.identifier.urihttp://hdl.handle.net/2299/4291
dc.descriptionOriginal article can be found at: http://www.worldscinet.com/ijait/mkt/archive.shtml Copyright World Scientific Publishing Company DOI: 10.1142/S0218213006002606 [Full text of this article is not available in the UHRA]
dc.description.abstractWe investigate the circuit complexity of classification problems in a machine learning setting, i.e. we attempt to find some rule that allows us to calculate a priori the number of threshold gates that is sufficient to achieve a small error rate after training a circuit on sample data ${\mathcal S}_L$. The particular threshold gates are computed by a combination of the classical perceptron algorithm with a specific type of stochastic local search. The circuit complexity is analysed for depth-two and depth-four threshold circuits, where we introduce a novel approach to compute depth-four circuits. For the problems from the UCI Machine Learning Repository we selected and investigated, we obtain approximately the same size of depth-two and depth-four circuits for the best classification rates on test samples, where the rates differ only marginally for the two types of circuits. Based on classical results from threshold circuit theory and our experimental observations on problems that are not linearly separable, we suggest an upper bound of $8\cdot \sqrt{2^n/n}$ threshold gates as sufficient for a small error rate, where $n := \log|{\mathcal S}_L|$.en
dc.language.isoeng
dc.relation.ispartofInternational Journal on Artificial Intelligence Tools
dc.titleA Computational Study on Circuit Size vs. Circuit Depthen
dc.contributor.institutionSchool of Computer Science
dc.description.statusPeer reviewed
dc.relation.schoolSchool of Computer Science
dcterms.dateAccepted2006
rioxxterms.versionofrecordhttps://doi.org/10.1142/S0218213006002606
rioxxterms.typeJournal Article/Review
herts.preservation.rarelyaccessedtrue


Files in this item

FilesSizeFormatView

There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record