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Robust parameter estimation of finite mixture models with self-paced learning
Mini Dissertation (MSc (eScience))--University of Pretoria, 2022.
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| Format: | Thesis |
| Language: | English |
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University of Pretoria
2023
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| _version_ | 1867613588161560576 |
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| access_status_str | Open Access |
| author2 | Kanfer, F.H.J. (Frans) |
| author_browse | Kanfer, F.H.J. (Frans) |
| author_facet | Kanfer, F.H.J. (Frans) |
| collection | Thesis |
| dc_rights_str_mv | © 2022 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. |
| description | Mini Dissertation (MSc (eScience))--University of Pretoria, 2022. |
| format | Thesis |
| id | oai:repository.up.ac.za:2263/89376 |
| institution | University of Pretoria (South Africa) |
| language | English |
| last_indexed | 2026-06-10T12:38:30.789Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UPSpace — University of Pretoria Institutional Repository |
| publishDate | 2023 |
| publishDateRange | 2023 |
| publishDateSort | 2023 |
| publisher | University of Pretoria |
| publisherStr | University of Pretoria |
| record_format | dspace |
| source_str | UPSpace — University of Pretoria Institutional Repository |
| spelling | oai:repository.up.ac.za:2263/89376 Robust parameter estimation of finite mixture models with self-paced learning Kanfer, F.H.J. (Frans) u17005028@TUKS.co.za Millard, Sollie M. Kleynhans, Andre Ruben Gaussian Mixture model Finite Mixture Models Self-Paced Learning Clustering Unsupervised Learning UCTD Mini Dissertation (MSc (eScience))--University of Pretoria, 2022. Self-paced learning (SPL) is a training strategy that mitigates the impact of non-typical observations. SPL introduces observations in a meaningful order by considering the likelihood for each observation. The proposed algorithm considers a finite mixture model that includes a distributional structure for non-typical observations in the SPL weight calculation. Two new self-paced learning (SPL) algorithms is proposed for finite mixture models (FMM). This includes self-paced component learning FMMs and a self-paced learning algorithm that includes a distributional structure for non-typical observations. The properties of these algorithms are presented through a simulation study along with an application on real data. A comparison is made with the properties of well known models. The algorithms shows a reduction in parameter estimation bias which indicates an improvement in the estimation accuracy of the parameters. DSI-NICIS National e-Science Postgraduate Teaching and Training Platform (NEPTTP) Statistics MSc (eScience) Unrestricted 2023-02-09T13:16:10Z 2023-02-09T13:16:10Z 2024 2022 Dissertation * A2023 https://repository.up.ac.za/handle/2263/89376 en © 2022 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. application/pdf University of Pretoria |
| spellingShingle | Gaussian Mixture model Finite Mixture Models Self-Paced Learning Clustering Unsupervised Learning UCTD Robust parameter estimation of finite mixture models with self-paced learning |
| title | Robust parameter estimation of finite mixture models with self-paced learning |
| title_full | Robust parameter estimation of finite mixture models with self-paced learning |
| title_fullStr | Robust parameter estimation of finite mixture models with self-paced learning |
| title_full_unstemmed | Robust parameter estimation of finite mixture models with self-paced learning |
| title_short | Robust parameter estimation of finite mixture models with self-paced learning |
| title_sort | robust parameter estimation of finite mixture models with self paced learning |
| topic | Gaussian Mixture model Finite Mixture Models Self-Paced Learning Clustering Unsupervised Learning UCTD |
| url | https://repository.up.ac.za/handle/2263/89376 |