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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2405.05492 |
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| _version_ | 1866929693348855808 |
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| author | Jung, Inkee Lau, Siu-Cheong |
| author_facet | Jung, Inkee Lau, Siu-Cheong |
| contents | In this paper,we develop a local-to-global and measure-theoretical approach to understand datasets. The idea is to take network models with restricted domains as local charts of datasets. We develop the mathematical foundations for these structures, and show in experiments how it can be used to find fuzzy domains and to improve accuracy in data classification problems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_05492 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A logifold structure on measure space Jung, Inkee Lau, Siu-Cheong Differential Geometry Artificial Intelligence Machine Learning Probability 55N31, 53Z50, 68T07, 68T09, 60A10, 81P45, 94D05 In this paper,we develop a local-to-global and measure-theoretical approach to understand datasets. The idea is to take network models with restricted domains as local charts of datasets. We develop the mathematical foundations for these structures, and show in experiments how it can be used to find fuzzy domains and to improve accuracy in data classification problems. |
| title | A logifold structure on measure space |
| topic | Differential Geometry Artificial Intelligence Machine Learning Probability 55N31, 53Z50, 68T07, 68T09, 60A10, 81P45, 94D05 |
| url | https://arxiv.org/abs/2405.05492 |