Skip navigation
Run Run Shaw Library City University of Hong KongRun Run Shaw Library

Please use this identifier to cite or link to this item: http://dspace.cityu.edu.hk/handle/2031/9079
Full metadata record
DC FieldValueLanguage
dc.contributor.authorCheung, Ho Namen_US
dc.date.accessioned2019-01-29T04:58:47Z
dc.date.accessioned2019-02-12T06:54:04Z-
dc.date.available2019-01-29T04:58:47Z
dc.date.available2019-02-12T06:54:04Z-
dc.date.issued2018en_US
dc.identifier.other2018cschn545en_US
dc.identifier.urihttp://144.214.8.231/handle/2031/9079-
dc.description.abstractExtreme multi-label learning (XML) or extreme multi-label classification is no longer a theoretical problem due to the enormous growth of data. Those commonly used technologies each as web page tagging (web labelling) and product recommendation are typical example of using the techniques of extreme multi-labelling. Extreme multi-label learning aims to correctly tag a data point with the most relevant class labels by machine from an enormous set of class labels by training a classifier (Bhatia, Jain, Kar, Varma, & Jain, 2015). In this project, extensive experiments on benchmark datasets from "The Extreme Classification Repository" will be conducted. I aim to improve the currently existing extreme multi-labelling learning algorithm with using different deep neural network architectures. The result is satisfactory.en_US
dc.titleExtreme Multi-labelling with Neural Networksen_US
dc.contributor.departmentDepartment of Computer Scienceen_US
dc.description.supervisorSupervisor: Dr. Nutanong, Sarana; First Reader: Dr. Chan, Mang Tang; Second Reader: Prof. Jia, Xiaohuaen_US
Appears in Collections:Computer Science - Undergraduate Final Year Projects 

Files in This Item:
File SizeFormat 
fulltext.html148 BHTMLView/Open
Show simple item record


Items in Digital CityU Collections are protected by copyright, with all rights reserved, unless otherwise indicated.

Send feedback to Library Systems
Privacy Policy | Copyright | Disclaimer