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Providing IaaS resources automatically through prediction and monitoring approaches

date accessioned2020-03-12T20:54:42Z
date available2020-03-12T20:54:42Z
date issued2014
identifier other6912590.pdf
identifier urihttps://libsearch.um.ac.ir:443/fum/handle/fum/1030084?show=full
formatgeneral
languageEnglish
publisherIEEE
titleProviding IaaS resources automatically through prediction and monitoring approaches
typeConference Paper
contenttypeMetadata Only
identifier padid8155445
subject keywordslearning (artificial intelligence)
subject keywordssampling methods
subject keywordssupport vector machines
subject keywordsRUS method
subject keywordsSMOTE method
subject keywordsSVM
subject keywordsboundary ratio
subject keywordsclass imbalance learning
subject keywordsinstance learning
subject keywordslinear support vector machine
subject keywordsrandom under-sampling method
subject keywordsresampling methods
subject keywordssynthetic minority over-sampling technique
subject keywordsClassification algorithms
subject keywordsComputers
subject keywordsEducational institutions
subject keywordsGlass
subject keywordsVehicles
subject keywordsborderline ratio based sampling
subject keywordsimbalanced data
subject keywordssupport vector machine
identifier doi10.1109/ICCSE.2014.6926515
journal titleomputers and Communication (ISCC), 2014 IEEE Symposium on
filesize482998
citations0
contributor rawauthorda Silva Dias, Ariel , Nakamura, Luis H.V. , Estrella, Julio C. , Santana, Regina H.C. , Santana, Marcos J.


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