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dc.rights.licenseCC-BY-NC-ND
dc.contributor.advisorVreeswijk, G.A.W.
dc.contributor.advisorBroersen, J.M.
dc.contributor.authorDulek, R.H.
dc.date.accessioned2013-05-21T17:01:09Z
dc.date.available2013-05-21
dc.date.available2013-05-21T17:01:09Z
dc.date.issued2013
dc.identifier.urihttps://studenttheses.uu.nl/handle/20.500.12932/13002
dc.description.abstractThe best method to use for machine learning depends on the problem. This thesis considers one aspect of machine learning problems: How do the properties of the hypothesis space affect machine learning? It collects academic advances on how dimensionality and representational capacity of the space and the presence of local optima affect machine learning. Useful additions to generic machine learning methods are listed that deal with these properties. The result is a collective overview on how to design a machine learning process that uses these properties of the hypothesis space.
dc.description.sponsorshipUtrecht University
dc.format.extent416683 bytes
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.titleProperties of the Hypothesis Space and their Effect on Machine Learning
dc.type.contentBachelor Thesis
dc.rights.accessrightsOpen Access
dc.subject.keywordsmachine learning
dc.subject.keywordshypothesis space
dc.subject.keywordsdimensionality
dc.subject.keywordsrepresentational capacity
dc.subject.keywordslocal optima
dc.subject.keywordscomplexity
dc.subject.courseuuKunstmatige Intelligentie


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