Author:
Patwary Muhammed J. A.,Wang Xi-Zhao,Yan Dasen
Funder
National Natural Science Foundation of China
Natural Science Foundation of SZU
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Computational Theory and Mathematics,Theoretical Computer Science,Software
Reference54 articles.
1. Zhu, X., Goldberg, A.: Introduction to semi-supervised learning. Synth. Lect. Artif. Intell. Mach. Learn. 3(1), 1–130 (2009)
2. Seeger, M.: Learning with Labeled and Unlabeled Data (Tech. Rep.). Edinburgh, UK: Institute for Adaptive and Neural Computation, University of Edinburgh (2000)
3. Chawla, N.V., Karakoulas, G.: Learing from labeled and unlabeled data: an empirical study across techniques and domain. J. Artif. Intell. Res. 23, 331–366 (2005)
4. Zhou, Z.-H., Zhan, D.-C., Yang, Q.: Semi-supervised learning with very few labeled training examples. In: AAAI, pp. 675–680 (2007)
5. Zadeh, L.A.: Probability measures of fuzzy events. J. Math. Anal. Appl. 23(2), 421–427 (1968)
Cited by
20 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
1. Crop Yield Prediction: A Fusion of IoT and Machine Learning for Precision Agriculture;2023 26th International Conference on Computer and Information Technology (ICCIT);2023-12-13
2. Impact of Fuzziness for Skin Lesion Classification with Transformer-Based Model;2023 International Conference on Computing, Electronics & Communications Engineering (iCCECE);2023-08-14
3. Discriminative sparse least square regression for semi-supervised learning;Information Sciences;2023-07
4. On a Parametric Measure of Vagueness;IEEE Transactions on Fuzzy Systems;2023-01
5. Fuzziness Based Semi-supervised Deep Learning for Multimodal Image Classification;Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering;2023