Graph-based Semi-Supervised Classification
直推式学习: transductive learning
参考: Graph-based Semi-Supervised Classification 与 半监督学习.
- LLGC: Learning with Local and Global Consistency1
- Mean Teacher: Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results2
- GRFH: Semi-Supervised Learning Using Gaussian Fields and Harmonic Functions3
- RMGT: Robust Multi-Class Transductive Learning with Graphs4
- GCN: Semi-Supervised Classification With Graph Convolutional Networks5
K 近邻 + CNN
- Condensed NN 综述: Survey of Nearest Neighbor Condensing Techniques6
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Zhou D, Bousquet O, Lal T N, et al. Learning with Local and Global Consistency[C]. neural information processing systems, 2003: 321-328. ↩
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Tarvainen A, Valpola H. Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results[J]. neural information processing systems, 2017: 1195-1204. ↩
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Zhu X, Ghahramani Z, Lafferty J D, et al. Semi-supervised learning using Gaussian fields and harmonic functions[C]. international conference on machine learning, 2003: 912-919. ↩
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Liu W, Chang S. Robust multi-class transductive learning with graphs[C]. computer vision and pattern recognition, 2009: 381-388. ↩
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Kipf T N, Welling M. Semi-Supervised Classification with Graph Convolutional Networks[J]. international conference on learning representations, 2017. ↩
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Amal M, Ahmed B. Survey of Nearest Neighbor Condensing Techniques[J]. International Journal of Advanced Computer Science and Applications, 2011, 2(11). ↩