Semi-supervised learning of tree-structured RBF networks using co-training

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 5163 LNCS, Issue PART 1, 2008, Pages 79-88

  Abdel Hady, Mohamed F ^a   Schwenker, Friedhelm ^a   Palm, Günther ^a  

^a UNIVERSITY OF ULM   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION TASKS; CO-TRAINING; DATA LABELING; FEATURE SETS; LABELED DATA; MULTI-CLASS; MULTI-CLASS PROBLEMS; RBF NETWORK; REAL-WORLD; SEMI-SUPERVISED LEARNING; SEMI-SUPERVISED LEARNING METHODS; STRUCTURED APPROACH; SUB-PROBLEMS; UNLABELED DATA;

BACKPROPAGATION; EDUCATION; LEARNING SYSTEMS; NEURAL NETWORKS; PATTERN RECOGNITION;

LABELS;

CLASSIFICATION; EDUCATION; LABELS; NEURAL NETWORKS; PATTERN RECOGNITION;

EID: 58949097230     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-87536-9_9     Document Type: Conference Paper

References (12)

1. Kahsay, L., Schwenker, F., Palm, G.: Comparison of multiclass SVM decomposition schemes for visual object recognition. In: Kropatsch, W.G., Sablatnig, R., Hanbury, A. (eds.) DAGM 2005. LNCS, vol. 3663, pp. 334-341. Springer, Heidelberg (2005)
2. Hrycej, T.: Modular Learning in Neural Networks: A Modularized Approach to Neural Network Classification. John Wiley & Sons, Inc., NY (1992)
3. Seeger, M.: Learning with labeled and unlabeled data, Technical report (2002)
4. Ghani, R.: Using error-correcting codes with co-training for text classification with a large number of categories. In: Proceedings of Workshop on Text Mining at the First IEEE Conference on Data Mining (2001)
5. Blum, A., Mitchell, T.: Combining labeled and unlabeled data with co-training. In: Proceedings of the the 11th annual conference on Computational Learning Theory, pp. 92-100. Morgan Kaufmann Publishers, San Francisco (1998)
6. 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. ACM, New York (2000)
7. Abdel Hady, M.F., Palm, G., Schwenker, F.: Multi-view forests of radial basis function networks based on dempster-shafer evidence theory. In: Verleysen, M. (ed.) Proceedings of the 16th European Symposium on Artificial Neural Networks (ESANN 2008), Bruges, Belgium, pp. 307-312. D-side publications (April 2008)
8. Schwenker, F., Kestler, H., Palm, G.: Three learning phases for radial basis function networks. Neural Networks 14, 439-458 (2001)
9. Dempster, A.P.: A generalization of bayesian inference. Journal of the Royal Statistical Society, 205-247 (1968)
10. Shafer, G.: A Mathematical Theory of Evidence. Princeton University Press, Princeton (1976)
11. Nene, S., Nayar, S., Murase, H.: Columbia object image library: Coil (1996)
12. Fay, R.: Feature selection and information fusion in hierarchical neural networks for iterative 3D-object recognition. PhD thesis, Ulm University (2007)