Transductive Confidence Machine and its application to Medical Data Sets
Fuente:
arXiv
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| Autore principale: | |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866914811446558720 |
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| author | Lindsay, David |
| author_facet | Lindsay, David |
| contents | The Transductive Confidence Machine Nearest Neighbours (TCMNN) algorithm and a supporting, simple user interface was developed. Different settings of the TCMNN algorithms' parameters were tested on medical data sets, in addition to the use of different Minkowski metrics and polynomial kernels. The effect of increasing the number of nearest neighbours and marking results with significance was also investigated. SVM implementation of the Transductive Confidence Machine was compared with Nearest Neighbours implementation. The application of neural networks was investigated as a useful comparison to the transductive algorithms. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_15988 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Transductive Confidence Machine and its application to Medical Data Sets Lindsay, David Machine Learning Artificial Intelligence The Transductive Confidence Machine Nearest Neighbours (TCMNN) algorithm and a supporting, simple user interface was developed. Different settings of the TCMNN algorithms' parameters were tested on medical data sets, in addition to the use of different Minkowski metrics and polynomial kernels. The effect of increasing the number of nearest neighbours and marking results with significance was also investigated. SVM implementation of the Transductive Confidence Machine was compared with Nearest Neighbours implementation. The application of neural networks was investigated as a useful comparison to the transductive algorithms. |
| title | Transductive Confidence Machine and its application to Medical Data Sets |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2405.15988 |