Towards Making Unlabeled Data Never Hurt
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[1] D. Rubin,et al. Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .
[2] Scott Kirkpatrick,et al. Optimization by simulated annealing: Quantitative studies , 1984 .
[3] V. Cerný. Thermodynamical approach to the traveling salesman problem: An efficient simulation algorithm , 1985 .
[4] Editors , 1986, Brain Research Bulletin.
[5] Emile H. L. Aarts,et al. Simulated Annealing: Theory and Applications , 1987, Mathematics and Its Applications.
[6] Anil K. Jain,et al. Algorithms for Clustering Data , 1988 .
[7] David J. Miller,et al. A Mixture of Experts Classifier with Learning Based on Both Labelled and Unlabelled Data , 1996, NIPS.
[8] Avrim Blum,et al. The Bottleneck , 2021, Monopsony Capitalism.
[9] Vladimir Vapnik,et al. Statistical learning theory , 1998 .
[10] Ayhan Demiriz,et al. Semi-Supervised Support Vector Machines , 1998, NIPS.
[11] Thorsten Joachims,et al. Transductive Inference for Text Classification using Support Vector Machines , 1999, ICML.
[12] Nikunj C. Oza,et al. Online Ensemble Learning , 2000, AAAI/IAAI.
[13] Tong Zhang,et al. The Value of Unlabeled Data for Classification Problems , 2000, ICML 2000.
[14] Avrim Blum,et al. Learning from Labeled and Unlabeled Data using Graph Mincuts , 2001, ICML.
[15] Jean-Michel Renders,et al. Combining Labelled and Unlabelled Data: A Case Study on Fisher Kernels and Transductive Inference for Biological Entity Recognition , 2002, CoNLL.
[16] Zoubin Ghahramani,et al. Semi-Supervised Learning Using Gaussian Fields and Harmonic Functions , 2003, ICML.
[17] Nello Cristianini,et al. Convex Methods for Transduction , 2003, NIPS.
[18] Bernhard Schölkopf,et al. Learning with Local and Global Consistency , 2003, NIPS.
[19] Shaoning Pang,et al. Transductive support vector machines and applications in bioinformatics for promoter recognition , 2003, International Conference on Neural Networks and Signal Processing, 2003. Proceedings of the 2003.
[20] Lei Wang,et al. Bootstrapping SVM active learning by incorporating unlabelled images for image retrieval , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..
[21] James C. Bezdek,et al. Convergence of Alternating Optimization , 2003, Neural Parallel Sci. Comput..
[22] Fabio Gagliardi Cozman,et al. Semi-Supervised Learning of Mixture Models , 2003, ICML.
[23] Alan L. Yuille,et al. The Concave-Convex Procedure , 2003, Neural Computation.
[24] Yoshua Bengio,et al. Semi-supervised Learning by Entropy Minimization , 2004, CAP.
[25] S. Dreyfus,et al. Thermodynamical Approach to the Traveling Salesman Problem : An Efficient Simulation Algorithm , 2004 .
[26] Sebastian Thrun,et al. Text Classification from Labeled and Unlabeled Documents using EM , 2000, Machine Learning.
[27] Zhi-Hua Zhou,et al. SETRED: Self-training with Editing , 2005, PAKDD.
[28] Xiaojin Zhu,et al. Semi-Supervised Learning Literature Survey , 2005 .
[29] Zhi-Hua Zhou,et al. Tri-training: exploiting unlabeled data using three classifiers , 2005, IEEE Transactions on Knowledge and Data Engineering.
[30] Alexander Zien,et al. Semi-Supervised Classification by Low Density Separation , 2005, AISTATS.
[31] Dale Schuurmans,et al. Unsupervised and Semi-Supervised Multi-Class Support Vector Machines , 2005, AAAI.
[32] Nitesh V. Chawla,et al. Learning From Labeled And Unlabeled Data: An Empirical Study Across Techniques And Domains , 2011, J. Artif. Intell. Res..
[33] Chih-Jen Lin,et al. Working Set Selection Using Second Order Information for Training Support Vector Machines , 2005, J. Mach. Learn. Res..
[34] Jason Weston,et al. Large Scale Transductive SVMs , 2006, J. Mach. Learn. Res..
[35] Mikhail Belkin,et al. Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples , 2006, J. Mach. Learn. Res..
[36] S. Sathiya Keerthi,et al. Branch and Bound for Semi-Supervised Support Vector Machines , 2006, NIPS.
[37] Alexander Zien,et al. Semi-Supervised Learning , 2006 .
[38] Xin Yao,et al. An analysis of diversity measures , 2006, Machine Learning.
[39] Alexander Zien,et al. A continuation method for semi-supervised SVMs , 2006, ICML.
[40] S. Sathiya Keerthi,et al. Deterministic annealing for semi-supervised kernel machines , 2006, ICML.
[41] Chih-Jen Lin,et al. A dual coordinate descent method for large-scale linear SVM , 2008, ICML '08.
[42] Shai Ben-David,et al. Does Unlabeled Data Provably Help? Worst-case Analysis of the Sample Complexity of Semi-Supervised Learning , 2008, COLT.
[43] S. Sathiya Keerthi,et al. Optimization Techniques for Semi-Supervised Support Vector Machines , 2008, J. Mach. Learn. Res..
[44] Zhi-Hua Zhou,et al. Semi-supervised learning by disagreement , 2010, Knowledge and Information Systems.
[45] Robert D. Nowak,et al. Unlabeled data: Now it helps, now it doesn't , 2008, NIPS.
[46] Shih-Fu Chang,et al. Graph transduction via alternating minimization , 2008, ICML '08.
[47] James T. Kwok,et al. Prototype vector machine for large scale semi-supervised learning , 2009, ICML '09.
[48] Ivor W. Tsang,et al. Maximum Margin Clustering Made Practical , 2007, IEEE Transactions on Neural Networks.
[49] Ivor W. Tsang,et al. Tighter and Convex Maximum Margin Clustering , 2009, AISTATS.
[50] Zhi-Hua Zhou,et al. Semi-supervised learning using label mean , 2009, ICML '09.
[51] Wei Liu,et al. Large Graph Construction for Scalable Semi-Supervised Learning , 2010, ICML.
[52] Maria-Florina Balcan,et al. A discriminative model for semi-supervised learning , 2010, J. ACM.
[53] Zhi-Hua Zhou,et al. Improving Semi-Supervised Support Vector Machines Through Unlabeled Instances Selection , 2010, AAAI.
[54] Ke Chen,et al. Semi-Supervised Learning via Regularized Boosting Working on Multiple Semi-Supervised Assumptions , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[55] Wei Liu,et al. Robust and Scalable Graph-Based Semisupervised Learning , 2012, Proceedings of the IEEE.
[56] Dirk Van,et al. Ensemble Methods: Foundations and Algorithms , 2012 .
[57] Ivor W. Tsang,et al. Convex and scalable weakly labeled SVMs , 2013, J. Mach. Learn. Res..
[58] Gavin Brown,et al. Ensemble Learning , 2010, Encyclopedia of Machine Learning and Data Mining.
[59] Xiaojin Zhu,et al. Semi-Supervised Learning , 2010, Encyclopedia of Machine Learning.