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61 | 61 | <td>Multivariate</td> |
62 | 62 | <td><a href="https://functional-data-clustering.github.io/Tutorials/REM_slides.pdf" target="_blank">Here</a></td> |
63 | 63 | <td><a href="https://pypi.org/project/REMclust/" target="_blank">PyPI</a></td> |
64 | | - <td>The <a href="http://www.tara.tcd.ie/bitstream/handle/2262/101920/REM_SIAM.pdf?sequence=5&isAllowed=y" target="_blank">Reinforced EM</a> (REM) algorithm provides an efficient solution to the initilization problem of the EM algorithm for clustering with Gaussian mixture models.<br> |
| 64 | + <td>The <a href="https://epubs.siam.org/doi/abs/10.1137/1.9781611977653.ch14" target="_blank">Reinforced EM</a> (REM) algorithm provides an efficient solution to the initilization problem of the EM algorithm for clustering with Gaussian mixture models.<br> |
65 | 65 | It initializes the Gaussian means with density-peak exemplars in the data.</td> |
66 | 66 | </tr> |
67 | 67 | <tr> |
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