Linking Data Separation, Visual Separation, Classifier Performance Using Multidimensional Projections
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-66743-5_11
Reference44 articles.
1. Amorim, W., Falcão, A., Papa, J., Carvalho, M.: Improving semi-supervised learning through optimum connectivity. Pattern Recognit. 60, 72–85 (2016)
2. Amorim, W., et al.: Semi-supervised learning with connectivity-driven convolutional neural networks: Pattern Recognit. Lett. 128, 16–22 (2019)
3. Arazo, E., Ortego, D., Albert, P., O’Connor, N.E., McGuinness, K.: Pseudo-labeling and confirmation bias in deep semi-supervised learning. In: IJCNN, pp. 1–8. IEEE (2020)
4. Benato, B.C., Falcão, A.X., Telea, A.C.: Linking data separation, visual separation, and classifier performance using multidimensional projections (2023). https://github.com/barbarabenato/linking_ds_vs_cp_proj
5. Benato, B.C., Telea, A.C., Falcão, A.X.: Semi-supervised learning with interactive label propagation guided by feature space projections. In: Proceedings of SIBGRAPI, pp. 392–399 (2018)
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