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| DBSCAN× | 자기 지도 학습× | |
|---|---|---|
| 분야 | 머신러닝 | 머신러닝 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 1996 | 2018–2020 |
| 창시자≠ | Ester, M., Kriegel, H.-P., Sander, J. & Xu, X. | LeCun, Y. and community (formalized ~2018–2020) |
| 유형≠ | Density-based clustering algorithm | Representation learning paradigm |
| 원전≠ | Ester, M., Kriegel, H.-P., Sander, J. & Xu, X. (1996). A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. Proceedings of the 2nd KDD, 226–231. link ↗ | LeCun, Y. & Misra, I. (2022). Self-supervised learning: The dark matter of intelligence. Meta AI Blog. https://ai.facebook.com/blog/self-supervised-learning-the-dark-matter-of-intelligence/ link ↗ |
| 별칭≠ | DBSCAN Kümeleme, density-based clustering, density-based spatial clustering | SSL, self-supervised pre-training, pretext-task learning, unsupervised representation learning |
| 관련 | 3 | 3 |
| 요약≠ | DBSCAN is a density-based clustering algorithm, introduced by Ester, Kriegel, Sander and Xu in 1996, that groups together points lying in dense regions and flags points in sparse regions as noise. It is effective on noisy data and on clusters of irregular, non-spherical shapes. | Self-supervised learning (SSL) is a machine-learning paradigm that generates its own supervisory signal directly from unlabeled data by defining an auxiliary pretext task — such as predicting masked words, rotating images, or contrasting augmented views — and uses the learned representations as a powerful starting point for downstream tasks with minimal labeled examples. |
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