Novel Semi-Supervised Learning Models to Balance Data Inclusivity and Usability in Healthcare Applications

Novel Semi-Supervised Learning Models to Balance Data Inclusivity and Usability in Healthcare Applications

abstract: Semi-supervised learning (SSL) is sub-field of statistical machine learning that is useful for problems that involve having only a few labeled instances with predictor (X) and target (Y) information, and abundance of unlabeled instances that only have predictor (X) information. SSL harnes...

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Bibliographic Details
Other Authors: Gaw, Nathan (Author)
Format: Doctoral Thesis
Language:English
Published: 2019
Subjects:
Industrial engineering
Biomedical engineering
Bioinformatics
glioblastoma
graph sampling
migraine
particle swarm optimization
semi-supervised learning
telemonitoring
Online Access:http://hdl.handle.net/2286/R.I.54812

Internet

http://hdl.handle.net/2286/R.I.54812

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