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Pembelajaran Sifar Contoh (Few-shot Learning)

Pembelajaran sifar contoh ialah paradigma pembelajaran mesin yang melatih model untuk mengenali kelas baharu atau menyelesaikan tugasan baharu hanya daripada segelintir contoh berlabel — lazimnya satu hingga lima — dengan memanfaatkan pengetahuan terdahulu yang diperoleh daripada taburan latihan yang besar dan berkaitan. Ia amat relevan dalam domain di mana pelabelan adalah mahal, jarang, atau terhad secara struktur.

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Sumber

  1. Vinyals, O., Blundell, C., Lillicrap, T., Wierstra, D., & Kavukcuoglu, K. (2016). Matching Networks for One Shot Learning. Advances in Neural Information Processing Systems (NeurIPS), 29. link
  2. Finn, C., Abbeel, P., & Levine, S. (2017). Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks. Proceedings of the 34th International Conference on Machine Learning (ICML), PMLR 70:1126–1135. link

Cara memetik halaman ini

ScholarGate. (2026, June 3). Few-shot Learning (Meta-learning with Limited Labeled Examples). ScholarGate. https://scholargate.app/ms/machine-learning/few-shot-learning

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ScholarGateFew-shot Learning (Few-shot Learning (Meta-learning with Limited Labeled Examples)). Dicapai 2026-06-15 daripada https://scholargate.app/ms/machine-learning/few-shot-learning · Set data: https://doi.org/10.5281/zenodo.20539026