Fuzzy Semi-supervised Support Vector Machines

1. Aamodt, A., Plaza, E.: Case-Based Reasoning. In: Proc. MLnet Summer School on Machine Learning and Knowledge Acquisition, pp. 1–58 (1994)

   Google Scholar 

2. Balcan, M.F., Blum, A.: An augmented PAC model for semi-supervised learning (2005) (manuscript)

   Google Scholar 

3. Baluja, S.: Probabilistic modeling for face orientation discrimination: Learning from labeled and unlabeled data. Advances in Neural Information Processing Systems 11, 854–860 (1998)

   Google Scholar 

4. Bennett, K., Demiriz, A.: Semi-supervised support vector machines. Advances in Neural Information Processing Systems 11, 368–374 (1998)

   Google Scholar 

5. Bensaid, A.M., Hall, L.O., et al.: Partially supervised clustering for image segmentation. Pattern Recognition 29(5), 859–871 (1996)

   Article  Google Scholar 

6. Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function Algorithms. Kluwer Academic Publishers, Norwell (1981)

   Book  MATH  Google Scholar 

7. Blum, A., Mitchell, T.: Combining labeled and unlabeled data with cotraining. In: Proceedings of the Eleventh Annual Conference on Computational Learning Theory, pp. 92–100 (1998)

   Google Scholar 

8. Bobrowski, L., Bezdek, J.C.: C-means clustering with the l l and l8 Norms. IEEE Transactions on, Systems, Man and Cybernetics 21(3), 545–554 (1991)

   Article  MathSciNet  MATH  Google Scholar 

9. Booker, L.B., Goldberg, D.E., et al.: Classifier systems and genetic algorithms. Artificial Intelligence 40(1-3), 235–282 (1989)

   Article  Google Scholar 

10. Cao, L.J., Lee, H.P., et al.: Modified support vector novelty detector using training data with outliers. Pattern Recognition Letters 24(14), 2479–2487 (2003)

    Article  MATH  Google Scholar 

11. Chapelle, O., Sindhwani, V., et al.: Branch and Bound for Semi- Supervised Support Vector Machines. In: Advances in Neural Information Processing Systems, (NIPS) (2006)

    Google Scholar 

12. Cheeseman, P., Freeman, D., et al.: Autoclass: A Bayesian classiciation system. In: Proceedings of the Fifth International Conference on Machine Learning, pp. 54–64 (1988)

    Google Scholar 

13. Collins, M., Singer, Y.: Unsupervised models for named entity classification. In: Proceedings of the Joint SIGDAT Conference on Empirical Methods in Natural Language Processing and Very Large Corpora, pp. 189–196 (1999)

    Google Scholar 

14. Cover, T., Hart, P.: Nearest neighbor pattern classification. IEEE Transactions on Information Theory 13(1), 21–27 (1967)

    Article  MATH  Google Scholar 

15. Cozman, F.G., Cohen, I., et al.: Semi-Supervised Learning of Mixture Models and Bayesian Networks. In: Proceedings of the Twentieth International Conference of Machine Learning (2003)

    Google Scholar 

16. Dagan, I., Engelson, S.P.: Committee-based sampling for training probabilistic classifiers. In: Proceedings of the Twelfth International Conference on Machine Learning, pp. 150–157 (1995)

    Google Scholar 

17. Day, W.H.E., Edelsbrunner, H.: Efficient algorithms for agglomerative hierarchical clustering methods. Journal of Classification 1(1), 7–24 (1984)

    Article  MATH  Google Scholar 

18. Dempster, A.P., Laird, N.M., et al.: Maximum Likelihood from Incomplete Data via the EM Algorithm. Journal of the Royal Statistical Society. Series B (Methodological) 39(1), 1–38 (1977)

    MathSciNet  MATH  Google Scholar 

19. Ester, M., Kriegel, H.P., et al.: A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. In: Proc. 2nd Int. Conf. on Knowledge Discovery and Data Mining, pp. 226–231. AAAI Press, Portland (1996)

    Google Scholar 

20. Freund, Y., Seung, H.S., et al.: Selective Sampling Using the Query by Committee Algorithm. Machine Learning 28(2), 133–168 (1997)

    Article  MATH  Google Scholar 

21. Fung, G., Mangasarian, O.: Semi-supervised support vector machines for unlabeled data classification (Technical Report 99-05). Data mining Institute, University of Wisconsin at Madison, Madison, WI (1999)

    Google Scholar 

22. Guyon, I., Matic, N., et al.: Discovering informative patterns and data cleaning. Advances in Knowledge Discovery and Data Mining Table of Contents, 181–203 (1996)

    Google Scholar 

23. Huang, W., Lippmann, R.: Neural net and traditional classifiers. Neural Information Processing Systems, 387–396 (1988)

    Google Scholar 

24. Joachims, T.: Making large-Scale SVM Learning Practical. In: Scoelkopf, B., Burges, C., Smola, A. (eds.) Advances in Kernel Methods-Support Vector Learning (1999)

    Google Scholar 

25. Joachims, T.: Transductive inference for text classification using support vector machines. In: Proceedings of the Sixteenth International Conference on Machine Learning, pp. 200–209 (1999)

    Google Scholar 

26. Jones, R.: Learning to Extract Entities from Labeled and Unlabeled Text, University of Utah (2005)

    Google Scholar 

27. Kaelbling, L.P., Littman, M.L., et al.: Reinforcement Learning: A Survey. Arxiv preprint cs.AI/9605103 (1996)

    Google Scholar 

28. Kockelkorn, M., Luneburg, A., et al.: Using transduction and multi-view learning to answer emails. In: Proceedings of the European Conference on Principle and Practice of Knowledge Discovery in Databases, pp. 266–277 (2003)

    Google Scholar 

29. Kohonen, T.: Self-organized formation of topologically correct feature maps. Biological Cybernetics 43(1), 59–69 (1982)

    Article  MathSciNet  MATH  Google Scholar 

30. Koller, D., Sahami, M.: Hierarchically classifying documents using very few words. In: Proceedings of the Fourteenth International Conference on Machine Learning, pp. 170–178 (1997)

    Google Scholar 

31. Langley, P., Simon, H.A.: Applications of machine learning and rule induction. Communications of the ACM 38(11), 54–64 (1995)

    Article  Google Scholar 

32. Letters, P.R.: Training algorithms for fuzzy support vector machines with noisy data. Pattern Recognition Letters 25(14), 1647–1656 (2004)

    Article  Google Scholar 

33. Liere, R., Tadepalli, P.: Active learning with committees for text categorization. In: Proceedings of the Fourteenth National Conference on Artificial Intelligence, pp. 591–596 (1997)

    Google Scholar 

34. Lin, C.F., Wang, S.D.: Fuzzy support vector machines. IEEE Transactions on Neural Networks 13(2), 464–471 (2002)

    Article  Google Scholar 

35. Littlestone, N.: Learning quickly when irrelevant attributes abound: A new linear-threshold algorithm. Machine Learning 2(4), 285–318 (1988)

    Google Scholar 

36. McCallum, A., Nigam, K.: Employing EM in pool-based active learning for text classification. In: Proceedings of ICML 1998, 15th International Conference on Machine Learning, pp. 350–358 (1998)

    Google Scholar 

37. Mitchell, T.: The role of unlabeled data in supervised learning. In: Proceedings of the Sixth International Colloquium on Cognitive Science (1999)

    Google Scholar 

38. Mladenic, D.: Learning word normalization using word suffix and context from unlabeled data. In: Proceedings of the Nineteenth International Conference on Machine Learning Table of Contents, pp. 427–434 (2002)

    Google Scholar 

39. Nigam, K., Ghani, R.: Analyzing the effectiveness and applicability of co-training. In: Proceedings of the Ninth International Conference on Information and Knowledge Management, pp. 86–93 (2000)

    Google Scholar 

40. Nigam, K., McCallum, A.K., et al.: Text Classification from Labeled and Unlabeled Documents using EM. Machine Learning 39(2), 103–134 (2000)

    Article  MATH  Google Scholar 

41. Quinlan, J.R.: Induction of decision trees. Machine Learning 1(1), 81–106 (1986)

    Google Scholar 

42. Rosenberg, C., Hebert, M., et al.: Semi-supervised selftraining of object detection models. In: Seventh IEEE Workshop on Applications of Computer Vision, vol. 1, pp. 29–36 (2005)

    Google Scholar 

43. Schohn, G., Cohn, D.: Less is more: Active learning with support vector machines. In: Proceedings of the Seventeenth International Conference on Machine Learning, vol. 282, pp. 285–286 (2000)

    Google Scholar 

44. Seung, H.S., Opper, M., et al.: Query by committee. In: Proceedings of the Fifth Annual Workshop on Computational Learning Theory, pp. 287–294 (1992)

    Google Scholar 

45. Sheng-de Wang, C.L.: Training algorithms for fuzzy support vector machines with noisy data. In: IEEE 13th Workshop on, Neural Networks for Signal Processing NNSP 2003, pp. 517–526 (2003)

    Google Scholar 

46. Tong, S., Koller, D.: Support vector machine active learning with applications to text classification. The Journal of Machine Learning Research 2, 45–66 (2002)

    MATH  Google Scholar 

47. Vapnik, V., Golowich, S., et al.: Support vector method for function approximation, regression estimation, and signal processing. Advances in Neural Information Processing Systems 9, 281–287 (1997)

    Google Scholar 

48. Vapnik, V.N.: Statistical learning theory. Wiley, New York (1998)

    MATH  Google Scholar 

49. Wang, W., Yang, J., et al.: STING: A Statistical Information Grid Approach to Spatial Data Mining. In: Proceedings of the 23rd International Conference on Very Large Data Bases, pp. 186–195 (1997)

    Google Scholar 

50. Xu, L., Schuurmans, D.: Unsupervised and Semi-supervised Multiclass Support Vector Machines. In: AAAI 2005, Pittsburgh, PA, pp. 904–910 (2005)

    Google Scholar 

51. Yarowsky, D.: Unsupervised word sense disambiguation rivalling supervised methods. In: Proceedings of the 33rd conference on Association for Computational Linguistics, pp. 189–196 (1995)

    Google Scholar 

52. Zhang, X.: Using class-center vectors to build support vector machines. In: Proceedings of the 1999 IEEE Signal Processing Society Workshop on Neural Networks for Signal Processing IX, pp. 3–11 (1999)

    Google Scholar 

53. Zhou, Z.-H., Zhan, D.-C., Yang, Q.: Semi-supervised learning with very few labeled training examples. In: Twenty-Second AAAI Conference on Artificial Intelligence, (AAAI 2007) (2007)

    Google Scholar 

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