Please use this identifier to cite or link to this item: http://localhost:8080/xmlui/handle/123456789/6649
Full metadata record
DC FieldValueLanguage
dc.contributor.authorAshwini Doke-
dc.date.accessioned2022-10-14T14:05:00Z-
dc.date.available2022-10-14T14:05:00Z-
dc.date.issued2021-06-
dc.identifier.urihttp://localhost:8080/xmlui/handle/123456789/6649-
dc.description.abstractAutomated Machine Learning is an area ofresearch that has gainedlots ofresearch in the past few years. To build a high qualityrnodel for Machine learning we need technical experts who have good knowledge in exploring various machines learning algoritlun as well as to tune the hyperpararneters efficiently. The size ofdata is increasing, it is observed that data scientist cannot address this challenging tasks due to lack ofexpertise and experience in the respective domain. So, there is a need to automate such crucial task inthe domain ofmachine learning. Metaleaming is "learning to learn" like human expertise. This paper is initial survey of ongoing research in field of Meta Learning and AutoMLen_US
dc.language.isoen_USen_US
dc.publisherIEEE Xploreen_US
dc.subjectComputer Science / Information Scienceen_US
dc.titleSurvey on Automated Machine Learning (AutoML) and Meta learningen_US
dc.title.alternativeComputer Science / Information Scienceen_US
dc.typeWorking Paperen_US
Appears in Collections:2021

Files in This Item:
File Description SizeFormat 
5. PAPER5ISE.pdf671.23 kBAdobe PDFView/Open


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.