Towards Practical Large Scale Non-Linear Semi-Supervised Learning with Balancing Constraints

Author:

Gao Zhengqing1,Wu Huimin2,Takáč Martin1,Gu Bin1

Affiliation:

1. Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, UAE

2. Nanjing University of Information Science & Technology, Nanjing, China

Publisher

ACM

Reference35 articles.

1. Quasi-Monte Carlo Feature Maps for Shift-Invariant Kernels;Avron Haim;Journal of Machine Learning Research,2016

2. Andrew Carlson , Justin Betteridge , Richard C. Wang , Estevam R. Hruschka , and Tom M. Mitchell . 2010. Coupled Semi-Supervised Learning for Information Extraction . In Proceedings of the Third ACM International Conference on Web Search and Data Mining ( New York, New York, USA) (WSDM '10). Association for Computing Machinery, New York, NY, USA, 101--110. https://doi.org/10.1145/17 1848 7.1718501 10.1145/1718487.1718501 Andrew Carlson, Justin Betteridge, Richard C. Wang, Estevam R. Hruschka, and Tom M. Mitchell. 2010. Coupled Semi-Supervised Learning for Information Extraction. In Proceedings of the Third ACM International Conference on Web Search and Data Mining (New York, New York, USA) (WSDM '10). Association for Computing Machinery, New York, NY, USA, 101--110. https://doi.org/10.1145/1718487.1718501

3. Luigi Carratino Alessandro Rudi and Lorenzo Rosasco. 2018. Learning with sgd and random features. In Advances in Neural Information Processing Systems. 10213--10224. Luigi Carratino Alessandro Rudi and Lorenzo Rosasco. 2018. Learning with sgd and random features. In Advances in Neural Information Processing Systems. 10213--10224.

4. Training a Support Vector Machine in the Primal

5. A continuation method for semi-supervised SVMs

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