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Examining the performance of topic modeling techniques in Twitter trends extraction

date accessioned2020-03-12T19:40:09Z
date available2020-03-12T19:40:09Z
date issued2014
identifier other6799706.pdf
identifier urihttps://libsearch.um.ac.ir:443/fum/handle/fum/985558?show=full
formatgeneral
languageEnglish
publisherIEEE
titleExamining the performance of topic modeling techniques in Twitter trends extraction
typeConference Paper
contenttypeMetadata Only
identifier padid8101214
subject keywordsimage classification
subject keywordsobject detection
subject keywordspedestrians
subject keywordsprobability
subject keywordstraffic engineering computing
subject keywordsAdaboost
subject keywordsGPCs
subject keywordsGaussian process classifiers
subject keywordsICF classifiers
subject keywordsPiatt exponential scaling
subject keywordsinformative predictive variance
subject keywordsintegral channel features
subject keywordsintrospective classification
subject keywordsisotonic regression
subject keywordsobject detectors
subject keywordspedestrian detection
subject keywordsprobabilistic measures
subject keywordsreliability
subject keywordsEntropy
subject keywordsMATLAB
subject keywordsReliability
subject keywordsVisualization
identifier doi10.1109/SSPD.2014.6943310
journal titlenformation Networking (ICOIN), 2014 International Conference on
filesize167072
citations0
contributor rawauthorKurniati, M.N. , Woo-Jong Ryu , Alam, M.H. , Sangkeun Lee


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