Semi-supervised learning in initially labeled non-stationary environments with gradual drift

Proceedings of the International Joint Conference on Neural Networks

Volumn , Issue , 2012, Pages

  Dyer, Karl B ^a   Polikar, Robi ^a  

^a ROWAN UNIVERSITY   (United States)

Author keywords

alpha shape; concept drift; non stationary environment; semi supervised learning; shape offsets

Indexed keywords

ALPHA-SHAPES; CONCEPT DRIFTS; NON-STATIONARY ENVIRONMENT; SEMI-SUPERVISED LEARNING; SHAPE OFFSETS;

ALGORITHMS; NEURAL NETWORKS;

SUPERVISED LEARNING;

EID: 84865066012     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IJCNN.2012.6252541     Document Type: Conference Paper

References (29)

1. A.Kuh, T.Petsche, and R.L.Rivest, "Learning time-varying concepts," Proc.Conf.Advances Neural Inform.Process.Syst., pp. 183-189, 1990.
2. O.Chapelle, B.Scholkopf, and A.Zien, Semi-Supervised Learning. Cambridge, MA: MIT Press, 2006.
3. A.P.Dempster, N.M.Laird, and D.B.Rubin, "Maximum Likelihood from Incomplete Data via the EM Algorithm," J. Roy. Stat. Soc. Series B (Methodological), vol. 39, no. 1, pp. 1-38, 1977.
4. X.Zhu and A.Goldberg, Introduction to Semi-Supervised Learning Morgan & Claypool, 2009, pp. 31-32.
5. T.Joachims, "Transductive Inference for Text Classification using Support Vector Machines," Proc. 16th Int. Conf. Machine Learning, pp. 200-209, 1999.
6. V.Vapnik and A.Sterin, "On structural risk minimization for overall risk in a problem of pattern recognition," Automation and Remote Control, vol. 10, pp. 1495-1503, 1977.
7. D.Zhou, O.Bousquet, T.Lal, J.Weston, and B.Scholkopf, "Learning with Local and Global Consistency," in Advances in Neural Information Processing Systems Cambridge, MA: MIT Press, 2004, 16, pp. 321-328.
8. X.Zhu and Z.Ghahramani, "Learning from Labeled and Unlabeled Data with Label Propagation," Carnegie Mellon University, Pittsburgh, PA,CMU-CALD-02-107, 2002.
9. D.Obradovic, "On-line training of recurrent neural networks with continuous topology adaptation," IEEE Trans.Neural Netw., vol. 7, pp. 222-228, 1996.
10. G.Hulten, L.Spencer, and P.Domingos, "Mining Time-Changing Data Streams," Proc. 7th ACM SIGKDD Int. Conf. Knowledge Discovery Data Mining, pp. 97-106, 2001.
11. G.Widmer and M.Kubat, "Learning in the presence of concept drift and hidden contexts," Mach. Learning, vol. 23, no. 1, pp. 69-101, 1996.
12. A.Bifet and R.Gavalda, "Learning from Time-Changing Data with Adaptive Windowing," SIAM Int.Conf.Data Mining, pp. 443-448, 2007.
13. R.Elwell and R.Polikar, "Incremental Learning of Concept Drift in Nonstationary Environments," IEEE Trans. Neural Netw., vol. 22, pp. 1517-1531, 2011.
14. J.Kolter and M.Maloof, "Dynamic Weighted Majority: An Ensemble Method for Drifting Concepts," J. Mach. Learning Research, vol. 8, pp. 2755-2790, 2007.
15. W.Street and Y.Kim, "A streaming ensemble algorithm (SEA) for large-scale classification," Proc. 7th ACM SIGKDD Int. Conf. Knowledge Discovery Data Mining, pp. 377-382, 2001.
16. A.Goldberg, M.Li, and X.Zhu, "Online Manifold Regularization: A New Learning Setting and Empirical Study," Computer, vol. 5211, no. 1, pp. 393-407, 2008.
17. A.Goldberg, X.Zhu, A.Furger, and J.Xu, "OASIS: Online Active SemI-Supervised Learning," 25th Conf. Artificial Intell., 2011.
18. M.Masud, J.Gao, L.Khan, and J.Han, "A Practical Approach to Classify Evolving Data Streams: Training with Limited Amount of Labeled Data," IEEE 8th Int.Conf.Data Mining, pp. 929-934, 2008.
19. P.Li, X.Wu, and X.Hu, "Mining Recurring Concept Drifts with Limited Labeled Streaming Data," J.Mach.Learning Research Proc.2nd Asian Conf.Mach.Learning, pp. 241-252, 2010.
20. P.Zhang, X.Zhu, and L.Guo, "Mining Data Streams with Labeled and Unlabeled Training Examples," IEEE 9th Int. Conf. Data Mining, pp. 627-636, 2009.
21. G.Ditzler and R.Polikar, "Semi-supervised Learning in Nonstationary Environments," Proc. Int. Joint Conf. Neural Networks, pp. 2741-2748, 2011.
22. P.Zhang, X.Zhu, J.Tan, and L.Guo, "Classifier and Cluster Ensembles for Mining Concept Drifting Data Streams," IEEE 10th Int. Conf. Data Mining, pp. 1175-1180, 2010.
23. S.G.Akl and G.Toussaint, "Efficient convex hull algorithms for pattern recognition applications," Proc. 4th Int. Joint Conf. Pattern Recognition, pp. 483-487, 1978.
24. H.Edelsbrunner and E.Mucke, "Three-Dimensional Alpha Shapes," Assoc.Computing Machinery Trans.Graph., vol. 13, pp. 43-72, 1994.
25. M.Teichmann and M.Capps, "Surface Reconstruction with Anisotropic Density-Scaled Alpha Shapes," IEEE Visualization, pp. 67-72, 1998.
26. O.Aichholzer, D.Alberts, F.Aurenhammer, and B.Gaertner, "Straight skeletons of simple polygons," Proc. 4th Int. Symp. LIESMARS, pp. 114-124, 1995.
27. X.Zhu, "Semi-Supervised Learning Literature Survey," University of Wisconsin, Madison, WI,Computer Sciences TR 1530, 2008.
28. L.Xu and D.Schuurmans, "Unsupervised and Semi-supervised Multi-class Support Vector Machines," Proc. 20th Nat. Conf. Artificial Intell., vol. 2, pp. 904-910, 2005.
29. A.Gonopolskiy, B.Nash, B.Avery, and J.Thomas, "Experimenting with Multi-Class Semi-Supervised Support Vector Machines and High-Dimensional Datasets," unpublished.