Handwriting Recognition with Transductive Confidence Machines

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dc.contributor.advisorSprinkhuizen-Kuyper, I.G.
dc.contributor.advisorVuurpijl, L.G.
dc.contributor.authorPinxteren, Y.L.F.H. van
dc.date.issued2009-07-20
dc.description.abstractThe recently introduced transductive confidence machines (TCMs) framework allows to extend classifiers such that their performance can be set by the user prior to classification. In this paper we apply the TCM framework, with a plugged in k-nearest neighbor classifer, to the domain of (on-line) handwriting recognition. First, we modify the original TCM algorithm to make it much more efficient. Then, we use this modified algorithm to classify the NicIcon database of iconic gestures. Results show that the modified TCM algorithm is a promising way to classify handwriting.en_US
dc.identifier.urihttp://theses.ubn.ru.nl/handle/123456789/63
dc.language.isoenen_US
dc.thesis.facultyFaculteit der Sociale Wetenschappenen_US
dc.thesis.specialisationBachelor Artificial Intelligenceen_US
dc.thesis.studyprogrammeArtificial Intelligenceen_US
dc.thesis.typeBacheloren_US
dc.titleHandwriting Recognition with Transductive Confidence Machinesen_US
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