81. (Tutorial etc)
•
• NAACL 2006 Tutorial: Inductive Semi-supervised Learning
with Applicability to NLP, A. Sarkar and G. Haffari.
• ICML 2007 Tutorial: Semi-supervised Learning, Xiaojin Zhu.
• Blitzer, J. and Zhu, J. (2008). ACL 2008 tutorial on Semi-
Supervised learning. http://ssl-acl08.wikidot.com/.
•
• X. Zhu. Semi-supervised learning literature survey. Technical
report, Computer Sciences, University of Wisconsin-
Madison, 2007.
• Zhu, X. (2005). Semi-supervised learning with graphs.
Doctoral dissertation, Carnegie Mellon University. CMU-
LTI-05-192.
Thursday, January 21, 2010
82. (Generative model)
• Nigam, K., McCallum, A. K., Thrun, S., & Mitchell, T. (2000). Text
classification from labeled and unlabeled documents using EM.
Machine Learning, 39, 103–134.
• Liu, B., Lee, W. S.,Yu, P. S., & Li, X. (2002). Partially supervised
classification of text documents. Proceedings of the Nineteenth
International Conference on Machine Learning (ICML).
• Lee, W. S., & Liu, B. (2003). Learning with positive and unlabeled
examples using weighted logistic regression. Proceedings of the
Twentieth International Conference on Machine Learning (ICML).
• Denis, F., Gilleron, R., & Tommasi, M. (2002). Text classification from
positive and unlabeled examples. The 9th International Conference
on Information Processing and Management of Uncertainty in
Knowledge-Based Systems(IPMU).
Thursday, January 21, 2010
83. (TSVMs)
• Joachims, T. (1999). Transductive inference for text classification
using support vector machines. Proc. 16th International Conf.
on Machine Learning (pp. 200– 209). Morgan Kaufmann, San
Francisco, CA.
• Yuille, A.L., Rangara jan, A. The concave-convex procedure.
Neural Computation 15(4) (2003) 915–936.
• L. Wang, X. Shen, and W. Pan. On transductive support vector
machines. In J. Verducci, X. Shen, and J. Lafferty, editors,
Prediction and Discovery. American Mathematical Society, 2007.
• R. Collobert, et al. (2006). Large Scale Transductive SVMs.
Journal of Machine Learning Research 7:1687-1712.
Thursday, January 21, 2010
84. (Graph-based)
• Blum, A., & Chawla, S. (2001). Learning from labeled and unlabeled data using graph
mincuts. Proc. 18th International Conf. on Machine Learning.
• Zhu, X., Ghahramani, Z., & Lafferty, J. (2003a). Semi-supervised lear ning using
Gaussian fields and harmonic functions. The 20th International Conference on
Machine Learning (ICML).
• Shi, J., & Malik, J. (2000). Normalized cuts and image segmentation. IEEE Transactions
on Pattern Analysis and Machine Intelligence, 22, 888–905.
• Pang, B., & Lee, L. (2004). A sentimental education: Sentiment analysis using
subjectivity summarization based on minimum cuts. Proceedings of the Association
for Computational Linguistics (pp. 271–278).
• Goldberg, A., & Zhu, X. (2006). Seeing stars when there aren’t many s tars: Graph-
based semi-supervised learning for sentiment categorization. HLT-NAACL 2006
Workshop on Textgraphs: Graph-based Algorithms for Natural Language
Processing. New York, NY.
• Belkin, M., Niyogi, P., & Sindhwani, V. (2004b). Manifold regularization: A geometric
framework for learning from examples (Technical Report TR-2004-06). University
of Chicago.
• M. Belkin & P. Niyogi (2002). `Using Manifold Structure for Partially Labelled
Classification'. In NIPS, pp. 929+.
Thursday, January 21, 2010
85. ( )
• Seeger, M. (2001). Learning with labeled and unlabeled
data (Technical Report). University of Edinburgh.
• François Denis, Bat M, Universit'e De Lille I. PAC
Learning from Positive Statistical Queries. Proc. 9th
International Conference on Algorithmic Learning
Theory - ALT '98
•
•
Thursday, January 21, 2010
