Maximum margin semi-supervised learning with irrelevant data

Neural Networks - Tập 70 - Trang 90-102 - 2015
Haiqin Yang1,2, Kaizhu Huang3, Irwin King1,2, Michael R. Lyu1,2
1Shenzhen Key Laboratory of Rich Media Big Data Analytics and Application, Shenzhen Research Institute, The Chinese University of Hong Kong, Hong Kong
2Computer Science & Engineering, The Chinese University of Hong Kong, Hong Kong
3Xi’an Jiaotong-Liverpool University, Suzhou, Jiangsu, China

Tài liệu tham khảo

Belkin, 2006, Manifold regularization: A geometric framework for learning from labeled and unlabeled examples, Journal of Machine Learning Research, 7, 2399 Bennett, 1998, Semi-supervised support vector machines, 1831 Bertsekas, 1999 Bie, 2003, Convex methods for transduction, 73 Boyd, 2004 2006 Chen, X., Yang, H., King, I., & Lyu, M.R. (2015). Training-efficient feature map for shift-invariant kernels. In IJCAI. Buenos Aires, Argentina. Collobert, 2006, Large scale transductive SVMs, Journal of Machine Learning Research, 7, 1687 Dehdarbehbahani, 2014, Semi-supervised word polarity identification in resource-lean languages, Neural Networks, 58, 50, 10.1016/j.neunet.2014.05.018 Goldfarb, 1991, An o(n3l) primal interior point algorithm for convex quadratic programming, Mathematical Programming, 49, 325 Hastie, 2009 Hu, J., Yang, H., King, I., Lyu, M.R., & So, A.M.-C. (2015). Kernelized online imbalanced learning with fixed budgets. In AAAI. Austin Texss, USA, Jan. 25–30. Huang, 2014, Semi-supervised and unsupervised extreme learning machines, IEEE Transactions on Cybernetics, 44, 2405, 10.1109/TCYB.2014.2307349 Huang, K., Xu, Z., King, I., & Lyu, M. R. (2008). Semi-supervised learning from general unlabeled data. In Proceedings of the 8th IEEE international conference on data mining, ICDM 2008, December 15–19, 2008 (pp. 273–282). Pisa, Italy. Iosifidis, 2014, Regularized extreme learning machine for multi-view semi-supervised action recognition, Neurocomputing, 145, 250, 10.1016/j.neucom.2014.05.036 Joachims, T. (1999). Transductive inference for text classification using support vector machines. In International conference on machine learning, ICML(pp. 200–209). Bled, Slowenien. Lawrence, 2005, Semi-supervised learning via Gaussian processes, 753 Li, Y.-F., & Zhou, Z.-H. (2010). S4VM: Safe semi-supervised support vector machine. arXiv:1005.1545. Melacci, 2011, Laplacian support vector machines trained in the primal, Journal of Machine Learning Research, 12, 1149 Nigam, 2000, Text classification from labeled and unlabeled documents using EM, Machine Learning, 39, 103, 10.1023/A:1007692713085 Pan, 2010, A survey on transfer learning, IEEE Transactions on Knowledge and Data Engineering, 22, 1345, 10.1109/TKDE.2009.191 Schohn, 2000, Less is more: Active learning with support vector machines, 839 Schölkopf, 2002 Settles, 2010 Singh, A., Nowak, R.D., & Zhu, X. (2008). Unlabeled data: Now it helps, now it doesn’t. In NIPS (pp. 1513–1520). Sinz, F.H., Chapelle, O., Agarwal, A., & Schölkopf, B. (2008). An analysis of inference with the universum. In NIPS (pp. 1369–1376). Smola, A. J., Vishwanathan, S. V. N., & Hofmann, T. (2005). Kernel methods for missing variables. In Proceedings of the tenth international workshop on artificial intelligence and statistics. Sturm, 1999, Using sedumi 1.02, a matlab toolbox for optimization over symmetric cones, Optimization Methods & Software, 11, 625, 10.1080/10556789908805766 Valizadegan, H., & Jin, R. (2006). Generalized maximum margin clustering and unsupervised kernel learning. In Advances in neural information processing systems 19, proceedings of the twentieth annual conference on neural information processing systems, Vancouver, British Columbia, Canada, December 4–7, 2006(pp. 1417–1424). Vapnik, 1999 Vapnik, 2006 Wang, 2009, On efficient large margin semisupervised learning: Method and theory, Journal of the Royal Statistical Society: Series B, 10, 719 Weston, J., Collobert, R., Sinz, F.H., Bottou, L., & Vapnik, V. (2006). Inference with the universum. In ICML (pp. 1009–1016). Wolsey, 1998 Xu, Z., Jin, R., Zhu, J., King, I., & Lyu, M.R. (2007). Efficient convex relaxation for transductive support vector machine. In Advances in neural information processing systems 20, proceedings of the twenty-first annual conference on neural information processing systems, Vancouver, British Columbia, Canada, December 3–6, 2007. Yang, H., King, I., & Lyu, M.R. (2010). Multi-task learning for one-class classification. In IJCNN, Barcelona, Spain (pp. 1–8). Yang, 2011 Yang, 2013, Efficient online learning for multi-task feature selection, ACM Transactions on Knowledge Discovery from Data, 7, 1, 10.1145/2499907.2499909 Yang, 2011, Efficient sparse generalized multiple kernel learning, IEEE Transactions on Neural Networks, 22, 433, 10.1109/TNN.2010.2103571 Yang, H., Zhu, S., King, I., & Lyu, M.R. (2011). Can irrelevant data help semi-supervised learning, why and how? In CIKM2011, Glasgow, UK (pp. 937–946). Yuille, 2003, The concave–convex procedure, Neural Computation, 15, 915, 10.1162/08997660360581958 Zhang, D., Wang, J., Wang, F., & Zhang, C. (2008). Semi-supervised classification with universum. In SDM (pp. 323–333). Zhao, 2014, A general soft label based linear discriminant analysis for semi-supervised dimensionality reduction, Neural Networks, 55, 83, 10.1016/j.neunet.2014.03.005 Zhou, 2010, Semi-supervised learning by disagreement, Knowledge and Information Systems, 24, 415, 10.1007/s10115-009-0209-z Zhu, 2009