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Using Balanced Random Forests on Load Spectrum Data for Classifying Component Failures of a Hybrid Electric Vehicle Fleet

contributor authorBergmeir, Philipp
contributor authorNitsche, Christof
contributor authorNonnast, Jurgen
contributor authorBargende, Michael
contributor authorAntony, Peter
contributor authorKeller, Uwe
date accessioned2020-03-12T22:59:08Z
date available2020-03-12T22:59:08Z
date issued2014
identifier other7033148.pdf
identifier urihttps://libsearch.um.ac.ir:443/fum/handle/fum/1101450?show=full
formatgeneral
languageEnglish
publisherIEEE
titleUsing Balanced Random Forests on Load Spectrum Data for Classifying Component Failures of a Hybrid Electric Vehicle Fleet
typeConference Paper
contenttypeMetadata Only
identifier padid8243791
subject keywordsAuthentication
subject keywordsn Biometrics (access control)
subject keywordsn Control systems
subject keywordsn Databases
subject keywordsn Educational institutions
subject keywordsn Fingerprint recognition
subject keywordsn Vehicles
subject keywordsn authentication
subject keywordsn biometric system
subject keywordsn fingerprint
subject keywordsn motorists
subject keywordsn token-based
identifier doi10.1109/I4CT.2014.6914180
journal titleachine Learning and Applications (ICMLA), 2014 13th International Conference on
filesize323168
citations0


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