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dc.contributor.authorDomingo Soriano, Carlos
dc.contributor.authorGavaldà Mestre, Ricard
dc.contributor.authorWatanabe, O
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament de Ciències de la Computació
dc.date.accessioned2016-03-16T10:35:52Z
dc.date.available2016-03-16T10:35:52Z
dc.date.issued1998-06
dc.identifier.citationDomingo, C., Gavaldà, R., Watanabe, O. "Practical algorithms for on-line sampling". 1998.
dc.identifier.urihttp://hdl.handle.net/2117/84488
dc.description.abstractOne of the core applications of machine learning to knowledge discovery consists on building a function (a hypothesis) from a given amount of data (for instance a decision tree or a neural network) such that we can use it afterwards to predict new instances of the data. In this paper, we focus on a particular situation where we assume that the hypothesis we want to use for prediction is very simple, and thus, the hypotheses class is of feasible size. We study the problem of how to determine which of the hypotheses in the class is almost the best one. We present two on-line sampling algorithms for selecting hypotheses, give theoretical bounds on the number of necessary examples, and analize them exprimentally. We compare them with the simple batch sampling approach commonly used and show that in most of the situations our algorithms use much fewer number of examples.
dc.format.extent18 p.
dc.language.isoeng
dc.relation.ispartofseriesLSI-98-39-R
dc.subjectÀrees temàtiques de la UPC::Informàtica::Informàtica teòrica
dc.subject.otherMachine learning
dc.subject.otherKnowledge discovery
dc.subject.otherDecision trees
dc.subject.otherNeural networks
dc.subject.otherAlgorithms
dc.subject.otherOn-line sampling
dc.titlePractical algorithms for on-line sampling
dc.typeExternal research report
dc.rights.accessOpen Access
local.identifier.drac1893822
dc.description.versionPostprint (published version)
local.citation.authorDomingo, C.; Gavaldà, R.; Watanabe, O.


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