Providing IaaS resources automatically through prediction and monitoring approaches
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: 2014DOI: 10.1109/ICCSE.2014.6926515
Keyword(s): learning (artificial intelligence),sampling methods,support vector machines,RUS method,SMOTE method,SVM,boundary ratio,class imbalance learning,instance learning,linear support vector machine,random under-sampling method,resampling methods,synthetic minority over-sampling technique,Classification algorithms,Computers,Educational institutions,Glass,Vehicles,borderline ratio based sampling,imbalanced data,support vector machine
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Providing IaaS resources automatically through prediction and monitoring approaches
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| date accessioned | 2020-03-12T20:54:42Z | |
| date available | 2020-03-12T20:54:42Z | |
| date issued | 2014 | |
| identifier other | 6912590.pdf | |
| identifier uri | https://libsearch.um.ac.ir:443/fum/handle/fum/1030084?locale-attribute=en | |
| format | general | |
| language | English | |
| publisher | IEEE | |
| title | Providing IaaS resources automatically through prediction and monitoring approaches | |
| type | Conference Paper | |
| contenttype | Metadata Only | |
| identifier padid | 8155445 | |
| subject keywords | learning (artificial intelligence) | |
| subject keywords | sampling methods | |
| subject keywords | support vector machines | |
| subject keywords | RUS method | |
| subject keywords | SMOTE method | |
| subject keywords | SVM | |
| subject keywords | boundary ratio | |
| subject keywords | class imbalance learning | |
| subject keywords | instance learning | |
| subject keywords | linear support vector machine | |
| subject keywords | random under-sampling method | |
| subject keywords | resampling methods | |
| subject keywords | synthetic minority over-sampling technique | |
| subject keywords | Classification algorithms | |
| subject keywords | Computers | |
| subject keywords | Educational institutions | |
| subject keywords | Glass | |
| subject keywords | Vehicles | |
| subject keywords | borderline ratio based sampling | |
| subject keywords | imbalanced data | |
| subject keywords | support vector machine | |
| identifier doi | 10.1109/ICCSE.2014.6926515 | |
| journal title | omputers and Communication (ISCC), 2014 IEEE Symposium on | |
| filesize | 482998 | |
| citations | 0 | |
| contributor rawauthor | da Silva Dias, Ariel , Nakamura, Luis H.V. , Estrella, Julio C. , Santana, Regina H.C. , Santana, Marcos J. |