Analytics-as-a-Service (AaaS) Tool for Unstructured Data Mining
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: 2014DOI: 10.1109/WSC.2014.7020221
Keyword(s): Monte Carlo methods,sampling methods,stochastic processes,adaptive sampling rules,deterministic recursion,fully sequential Monte Carlo sampling method,noise-corrupted observations,stochastic recursions,stochastic simulation,unknown function,Adaptation models,Algorithm design and analysis,Approximation methods,Context,Context modeling,Random variables
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Analytics-as-a-Service (AaaS) Tool for Unstructured Data Mining
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| contributor author | Lomotey, R.K. , Deters, R. | |
| date accessioned | 2020-03-12T23:39:25Z | |
| date available | 2020-03-12T23:39:25Z | |
| date issued | 2014 | |
| identifier other | 6903489.pdf | |
| identifier uri | https://libsearch.um.ac.ir:443/fum/handle/fum/1119561 | |
| format | general | |
| language | English | |
| publisher | IEEE | |
| title | Analytics-as-a-Service (AaaS) Tool for Unstructured Data Mining | |
| type | Conference Paper | |
| contenttype | Metadata Only | |
| identifier padid | 8289595 | |
| subject keywords | Monte Carlo methods | |
| subject keywords | sampling methods | |
| subject keywords | stochastic processes | |
| subject keywords | adaptive sampling rules | |
| subject keywords | deterministic recursion | |
| subject keywords | fully sequential Monte Carlo sampling method | |
| subject keywords | noise-corrupted observations | |
| subject keywords | stochastic recursions | |
| subject keywords | stochastic simulation | |
| subject keywords | unknown function | |
| subject keywords | Adaptation models | |
| subject keywords | Algorithm design and analysis | |
| subject keywords | Approximation methods | |
| subject keywords | Context | |
| subject keywords | Context modeling | |
| subject keywords | Random variables | |
| identifier doi | 10.1109/WSC.2014.7020221 | |
| journal title | loud Engineering (IC2E), 2014 IEEE International Conference on | |
| filesize | 312840 | |
| citations | 0 |