View construction for multi-view semi-supervised learning

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

Volumn 6675 LNCS, Issue PART 1, 2011, Pages 595-601

  Sun, Shiliang ^a   Jin, Feng ^a   Tu, Wenting ^a  

^a EAST CHINA NORMAL UNIVERSITY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

CONSTRUCTION METHOD; FEATURE SELECTION; FEATURE SETS; FEATURE SUBSET; MULTI-VIEWS; MULTIPLE FEATURES; MULTIPLE VIEWS; SEMI-SUPERVISED LEARNING; SEMI-SUPERVISED LEARNING METHODS; SVM CLASSIFIERS;

EXPERIMENTS; FEATURE EXTRACTION; GENETIC ALGORITHMS; IMAGE RETRIEVAL; NEURAL NETWORKS;

SUPERVISED LEARNING;

EID: 79957848621     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-21105-8_69     Document Type: Conference Paper

References (12)

1. Chapelle, O., Schölkopf, B., Zien, A.: Semi-supervised Learning. MIT Press, Cambridge (2006)
2. Mitchell, M.: An Introduction to Genetic Algorithms. MIT Press, Cambridge (1998)
3. Blum, A., Mitchell, T.: Combining labeled and unlabeled data with co-training. In: Proceedings of the 11th Annual Conference on Computational Learning Theory, pp. 92-100 (1998)
4. Sun, S.: Semantic features for multi-view semi-supervised and active learning of text classification. In: Proceedings of the IEEE International Conference on Data Mining Workshops, pp. 731-735 (2008)
5. Nigam, K., Ghani, R.: Analyzing the effective and applicability of co-training. In: Proceedings of the 9th International Conference on Information and Knowledge Management, pp. 86-93 (2000)
6. Zhou, Z., Li, M.: Tri-training: Exploiting unlabeled data using three classifiers. IEEE Transactions on Knowledge and Data Engineering 17(11), 1529-1541 (2005)
7. Zhou, Y., Goldman, S.: Democratic co-learning. In: Proceedings of the 16th IEEE International Conference on Tools with Artificial Intelligence, pp. 594-602 (2004)
8. Balcan, M., Blum, A., Yang, K.: Co-training and expansion: Towards bridging theory and practice. Advances in Neural Information Processing Systems 17, 89-96 (2005)
9. Sindhwani, V., Rosenberg, D.: An RKHS for multiview learning and manifold co-regularization. In: Proceedings of the 25th International Conference on Machine Learning, pp. 976-983 (2008)
10. Caruana, R.: Multitask learning. Machine Learning 28(1), 41-75 (1997)
11. Jin, F., Sun, S.: A multitask learning approach to face recognition based on neural networks. In: Fyfe, C., Kim, D., Lee, S.-Y., Yin, H. (eds.) IDEAL 2008. LNCS, vol. 5326, pp. 24-31. Springer, Heidelberg (2008)
12. Evgeniou, T., Pontil, M.: Regularized multi-task learning. In: Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 109-117 (2004)