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Ujifundishaji wa majirani k-karibu

Ujifundishaji wa majirani k-karibu (SSL-kNN) unachanganya upataji wa maarifa bila lebo na kihesabu kisicho na kigezo cha k-NN. Kidhibiti cha neva hufunzwa kwanza kupitia lengo la kujifundisha - kama vile upinzani au utabiri uliofichwa - ili sampuli zinazofanana kwa maana zikusanyike pamoja katika nafasi ya uwekaji. Utafutaji rahisi wa k-NN kwenye uwekaji huo kisha hupeana lebo za darasa, ikitumika kama uchunguzi mwepesi na kama kihesabu kinachofaa.

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Vyanzo

  1. Chen, T., Kornblith, S., Norouzi, M., & Hinton, G. (2020). A simple framework for contrastive learning of visual representations. In Proceedings of the 37th International Conference on Machine Learning (ICML), PMLR 119, 1597–1607. link
  2. Wu, Z., Xiong, Y., Yu, S. X., & Lin, D. (2018). Unsupervised feature learning via non-parametric instance discrimination. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 3733–3742. DOI: 10.1109/CVPR.2018.00393

Jinsi ya kunukuu ukurasa huu

ScholarGate. (2026, June 3). Self-supervised K-Nearest Neighbors (SSL-kNN). ScholarGate. https://scholargate.app/sw/machine-learning/self-supervised-k-nearest-neighbors

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ScholarGateSelf-supervised K-nearest neighbors (Self-supervised K-Nearest Neighbors (SSL-kNN)). Imepatikana 2026-06-15 kutoka https://scholargate.app/sw/machine-learning/self-supervised-k-nearest-neighbors · Seti ya data: https://doi.org/10.5281/zenodo.20539026