High Order Regularization for Semi-Supervised Learning of Structured Output Problems
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[1] Zoubin Ghahramani,et al. Combining active learning and semi-supervised learning using Gaussian fields and harmonic functions , 2003, ICML 2003.
[2] Ben Taskar,et al. Posterior Regularization for Structured Latent Variable Models , 2010, J. Mach. Learn. Res..
[3] Thomas Hofmann,et al. Large Margin Methods for Structured and Interdependent Output Variables , 2005, J. Mach. Learn. Res..
[4] Mikhail Belkin,et al. Maximum Margin Semi-Supervised Learning for Structured Variables , 2005, NIPS 2005.
[5] Bernhard Schölkopf,et al. Learning with Local and Global Consistency , 2003, NIPS.
[6] Mikhail Belkin,et al. Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples , 2006, J. Mach. Learn. Res..
[7] Sebastian Nowozin,et al. Structured Prediction and Learning in Computer Vision , 2011 .
[8] Sebastian Thrun,et al. Learning to Classify Text from Labeled and Unlabeled Documents , 1998, AAAI/IAAI.
[9] Alex Krizhevsky,et al. Learning Multiple Layers of Features from Tiny Images , 2009 .
[10] Luc Van Gool,et al. The Pascal Visual Object Classes (VOC) Challenge , 2010, International Journal of Computer Vision.
[11] Gideon S. Mann,et al. Generalized Expectation Criteria for Semi-Supervised Learning with Weakly Labeled Data , 2010, J. Mach. Learn. Res..
[12] H. J. Scudder,et al. Probability of error of some adaptive pattern-recognition machines , 1965, IEEE Trans. Inf. Theory.
[13] Marie-Pierre Jolly,et al. Interactive graph cuts for optimal boundary & region segmentation of objects in N-D images , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.
[14] Pietro Perona,et al. Caltech-UCSD Birds 200 , 2010 .
[15] Ulf Brefeld,et al. Semi-supervised learning for structured output variables , 2006, ICML.
[16] Vladimir Kolmogorov,et al. Graph cut based image segmentation with connectivity priors , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.
[17] Rayid Ghani,et al. Analyzing the effectiveness and applicability of co-training , 2000, CIKM '00.
[18] Estevam R. Hruschka,et al. Coupled semi-supervised learning for information extraction , 2010, WSDM '10.
[19] Alexander Zien,et al. Semi-Supervised Learning , 2006 .
[20] Yoshua Bengio,et al. Semi-supervised Learning by Entropy Minimization , 2004, CAP.
[21] Slav Petrov,et al. Efficient Graph-Based Semi-Supervised Learning of Structured Tagging Models , 2010, EMNLP.
[22] Ben Taskar,et al. Max-Margin Markov Networks , 2003, NIPS.
[23] Ben Taskar,et al. Graph-Based Posterior Regularization for Semi-Supervised Structured Prediction , 2013, CoNLL.
[24] Richard S. Zemel,et al. HOP-MAP: Efficient Message Passing with High Order Potentials , 2010, AISTATS.
[25] Xiaojin Zhu,et al. Introduction to Semi-Supervised Learning , 2009, Synthesis Lectures on Artificial Intelligence and Machine Learning.
[26] Pushmeet Kohli,et al. Robust Higher Order Potentials for Enforcing Label Consistency , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.
[27] Andrew McCallum,et al. Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data , 2001, ICML.
[28] Shih-Fu Chang,et al. Graph transduction via alternating minimization , 2008, ICML '08.
[29] Thorsten Joachims,et al. Transductive Inference for Text Classification using Support Vector Machines , 1999, ICML.
[30] Joachim M. Buhmann,et al. Weakly supervised semantic segmentation with a multi-image model , 2011, 2011 International Conference on Computer Vision.
[31] Alexander Zien,et al. Transductive support vector machines for structured variables , 2007, ICML '07.
[32] Bill Triggs,et al. Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).
[33] Dale Schuurmans,et al. Learning to Model Spatial Dependency: Semi-Supervised Discriminative Random Fields , 2006, NIPS.
[34] Qiang Yang,et al. A Survey on Transfer Learning , 2010, IEEE Transactions on Knowledge and Data Engineering.
[35] Tamir Hazan,et al. A Primal-Dual Message-Passing Algorithm for Approximated Large Scale Structured Prediction , 2010, NIPS.
[36] Xiaojin Zhu,et al. Semi-Supervised Learning Literature Survey , 2005 .
[37] Vladimir Kolmogorov,et al. "GrabCut": interactive foreground extraction using iterated graph cuts , 2004, ACM Trans. Graph..
[38] Ming-Wei Chang,et al. Guiding Semi-Supervision with Constraint-Driven Learning , 2007, ACL.
[39] Avrim Blum,et al. The Bottleneck , 2021, Monopsony Capitalism.
[40] Pushmeet Kohli,et al. A Principled Deep Random Field Model for Image Segmentation , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.
[41] Sebastian Nowozin,et al. Structured Learning and Prediction in Computer Vision , 2011, Found. Trends Comput. Graph. Vis..
[42] D. Sontag. 1 Introduction to Dual Decomposition for Inference , 2010 .
[43] Rahul Gupta,et al. Efficient inference with cardinality-based clique potentials , 2007, ICML '07.
[44] Shimon Ullman,et al. Class-Specific, Top-Down Segmentation , 2002, ECCV.
[45] Tommi S. Jaakkola,et al. Introduction to dual composition for inference , 2011 .
[46] Richard S. Zemel,et al. Structured Output Learning with High Order Loss Functions , 2012, AISTATS.