Dissimilarity in graph-based semi-supervised classification

Journal of Machine Learning Research

Volumn 2, Issue , 2007, Pages 155-162

  Goldberg, Andrew B ^a   Zhu, Xiaojin ^a   Wright, Stephen ^a  

^a University of Wisconsin   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CLASS LABELS; CONVEX PROBLEMS; GRAPH-BASED; LABELED AND UNLABELED DATA; MULTI-CLASS CLASSIFICATION; SEMI-SUPERVISED CLASSIFICATION;

ARTIFICIAL INTELLIGENCE; GRAPHIC METHODS;

SUPERVISED LEARNING;

EID: 79951767220     PISSN: 15324435     EISSN: 15337928     Source Type: Journal    
DOI: None     Document Type: Conference Paper

References (21)

1. Sugato Basu, Mikhail Bilenko, Arindam Banerjee, and Raymond J. Mooney. Probabilistic semisupervised clustering with constraints. In O. Chapelle, B. Schölkopf, and A. Zien, editors, Semi-Supervised Learning, pages 71-98. MIT Press, 2006.
2. Mikhail Belkin, Partha Niyogi, and Vikas Sindhwani. Manifold regularization: A geometric framework for learning from examples. Technical Report TR-2004-06, University of Chicago, 2004.
3. Olivier Chapelle, Alexander Zien, and Bernhard Schölkopf, editors. Semi-supervised learning. MIT Press, 2006.
4. W. Chu, V. Sindhwani, Z. Ghahramani, and S. S. Keerthi. Relational learning with gaussian processes. In Advances in NIPS, 2006.
5. Koby Crammer and Yoram Singer. On the algorithmic implementation of multiclass kernel-based vector machines. Journal of Machine Learning Research, 2:265-292, 2002.
6. Nizar Grira, Michel Crucianu, and Nozha Boujemaa. Unsupervised and semi-supervised clustering: a brief survey, 2004. in 'A Review of Machine Learning Techniques for Processing Multimedia Content', Report of the MUSCLE European Network of Excellence (FP6).
7. Yoonkyung Lee, Yi Lin, and Grace Wahba. Multicategory support vector machines, theory, and application to the classification of microarray data and satellite radiance data. Journal of the American Statistical Association, 99:67-81, 2004.
8. Christopher D. Manning and Hinrich Schütze. Foundations of Statistical Natural Language Processing. The MIT Press, Cambridge, Massachusetts, 1999.
9. Tony Mullen and Robert Malouf. A preliminary investigation into sentiment analysis for informal political discourse. In Proceedings of the AAAI Workshop on Analysis of Weblogs, 2006.
10. Pradeep Ravikumar and John Lafferty. Quadratic programming relaxations for metric labeling and markov random field MAP estimation. In ICML06, 23rd International Conference on Machine Learning, Pittsburgh, USA, 2006.
11. Matthias Seeger. Learning with labeled and unlabeled data. Technical report, University of Edinburgh, 2001.
12. Vikas Sindhwani, Partha Niyogi, and Mikhail Belkin. Beyond the point cloud: from transductive to semisupervised learning. In ICML05, 22nd International Conference on Machine Learning, 2005.
13. Jurgen van Gael and Xiaojin Zhu. Correlation clustering for crosslingual link detection. In International Joint Conference on Artificial Intelligence (IJCAI), 2007.
14. Kiri Wagstaff, Claire Cardie, Seth Rogers, and Stefan Schrödl. Constrained k-means clustering with background knowledge. In International Conference on Machine Learning (ICML), page 577, 2001.
15. Martin Wainwright, Tommi Jaakkola, and Alan Willsky. MAP estimation via agreement on (hyper)trees: Message passing and linear-programming approaches. IEEE Transactions on Information Theory, 51:3697-3717, 2005.
16. Yair Weiss and William Freeman. On the optimality of solutions of the max-product belief-propagation algorithm in arbitrary graphs. IEEE Transactions on Information Theory, 47, 2001.
17. J. Weston and C. Watkins. Multi-class support vector machines. Technical Report CSD-TR-98-04, Department of Computer Science, Royal Holloway, University of London, 1998.
18. Eric Xing, Andrew Ng, Michael Jordan, and Stuart Russell. Distance metric learning with application to clustering with side-information. In Advances in Neural Information Processing Systems (NIPS), 2002.
19. Xiaojin Zhu. Semi-supervised learning literature survey. Technical Report 1530, Computer Sciences, University of Wisconsin-Madison, 2005.http://www.cs. wisc.edu/-jerryzhu/pub/sslsurvey.pdf.
20. Xiaojin Zhu, Zoubin Ghahramani, and John Lafferty. Semi-supervised learning using Gaussian fields and harmonic functions. In ICML-03, 20th International Conference on Machine Learning, 2003.
21. Xiaojin Zhu and Andrew B. Goldberg. Semisupervised regression with order preferences. Technical Report TR1578, Dept. of Computer Sciences, University of Wisconsin-Madison, 2006.