Classification, Novelty Detection and Clustering for Point Pattern Data
Point pattern data, also known as multiple instance data or bags, are abundant in nature and applications. However, machine learning problems for point patterns have not received much attention. In this work, we solve three fundamental machine learning problems, namely classification, novelty detect...
| Main Author: | |
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| Format: | Thesis |
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Curtin University
2017
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| Online Access: | http://hdl.handle.net/20.500.11937/59025 |
| _version_ | 1848760395353817088 |
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| author | Tran, Quang Nhat |
| author_facet | Tran, Quang Nhat |
| author_sort | Tran, Quang Nhat |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Point pattern data, also known as multiple instance data or bags, are abundant in nature and applications. However, machine learning problems for point patterns have not received much attention. In this work, we solve three fundamental machine learning problems, namely classification, novelty detection, and clustering, for point pattern data using two approaches: one with knowledge of the underlying data model (model-based approach), and one without (distance-based approach). |
| first_indexed | 2025-11-14T10:15:06Z |
| format | Thesis |
| id | curtin-20.500.11937-59025 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T10:15:06Z |
| publishDate | 2017 |
| publisher | Curtin University |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-590252017-11-30T06:35:20Z Classification, Novelty Detection and Clustering for Point Pattern Data Tran, Quang Nhat Point pattern data, also known as multiple instance data or bags, are abundant in nature and applications. However, machine learning problems for point patterns have not received much attention. In this work, we solve three fundamental machine learning problems, namely classification, novelty detection, and clustering, for point pattern data using two approaches: one with knowledge of the underlying data model (model-based approach), and one without (distance-based approach). 2017 Thesis http://hdl.handle.net/20.500.11937/59025 Curtin University fulltext |
| spellingShingle | Tran, Quang Nhat Classification, Novelty Detection and Clustering for Point Pattern Data |
| title | Classification, Novelty Detection and Clustering for Point Pattern Data |
| title_full | Classification, Novelty Detection and Clustering for Point Pattern Data |
| title_fullStr | Classification, Novelty Detection and Clustering for Point Pattern Data |
| title_full_unstemmed | Classification, Novelty Detection and Clustering for Point Pattern Data |
| title_short | Classification, Novelty Detection and Clustering for Point Pattern Data |
| title_sort | classification, novelty detection and clustering for point pattern data |
| url | http://hdl.handle.net/20.500.11937/59025 |