Maximum margin semi-supervised learning with irrelevant data
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