dc.contributor.advisor | Arroyave, Raymundo | |
dc.creator | Kunselman, Courtney Jo | |
dc.date.accessioned | 2021-01-04T15:54:32Z | |
dc.date.available | 2021-01-04T15:54:32Z | |
dc.date.created | 2020-05 | |
dc.date.issued | 2020-02-19 | |
dc.date.submitted | May 2020 | |
dc.identifier.uri | https://hdl.handle.net/1969.1/191726 | |
dc.description.abstract | Uncovering links between processing conditions, microstructure, and properties is a central tenet of materials analysis. It is well known that microstructure determines properties, but expressing these structural features in a universal quantitative fashion has proved to be extremely difficult. Recent efforts have focused on training supervised learning algorithms to place microstructure images into predefined classes, but this approach assumes a level of a priori knowledge that may not always be available. This work expands this idea to the semi-supervised context in which class labels are known with confidence for only a fraction of the microstructures that represent the material system. It is shown that classifiers which perform well on both the high-confidence labeled data and the unlabeled, ambiguous data can be constructed by relying on the labeling consensus of a collection of semi-supervised learning methods. We also demonstrate the use of novel error estimation approaches for unlabeled data to establish robust confidence bounds on the classification performance over the entire microstructure space. | en |
dc.format.mimetype | application/pdf | |
dc.language.iso | en | |
dc.subject | Machine learning | en |
dc.subject | Microstructure classification | en |
dc.subject | Support vector machines | en |
dc.subject | Semi-supervised learning methods | en |
dc.subject | Unsupervised error estimation | en |
dc.title | Semi-supervised Learning Approaches to Class Assignment in Ambiguous Microstructures | en |
dc.type | Thesis | en |
thesis.degree.department | Materials Science and Engineering | en |
thesis.degree.discipline | Materials Science and Engineering | en |
thesis.degree.grantor | Texas A&M University | en |
thesis.degree.name | Master of Science | en |
thesis.degree.level | Masters | en |
dc.contributor.committeeMember | Braga-Neto, Ulisses | |
dc.contributor.committeeMember | Srivastava, Ankit | |
dc.type.material | text | en |
dc.date.updated | 2021-01-04T15:54:32Z | |
local.etdauthor.orcid | 0000-0003-4903-874X | |