Dynamical ensemble learning with model-friendly classifiers for domain adaptation

Proceedings - International Conference on Pattern Recognition

Volumn , Issue , 2012, Pages 1181-1184

  Tu, Wenting ^a   Sun, Shiliang ^a  

^a EAST CHINA NORMAL UNIVERSITY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

BASE MODELS; CLASSICAL METHODS; DOMAIN ADAPTATION; ENSEMBLE LEARNING; REAL DATA SETS; RESEARCH DIRECTIONS; TARGET DOMAIN; TEST EXAMPLES; UNDERLYING DISTRIBUTION;

SOFTWARE ENGINEERING;

PATTERN RECOGNITION;

EID: 84874573399     PISSN: 10514651     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (18)

1. Daume III, H. and Marcu, D.: Domain adaptation for statistical classifiers. Journal of Artificial Intelligence Research, 26(1), 101-126 (2006)
2. Mansour, Y. and Mohri, M. and Rostamizadeh, A.: Domain adaptation with multiple sources. Advances in Neural Information Processing Systems, 21, 1041-1048 (2009)
3. Opitz, D. and Maclin, R.: Popular ensemble methods: An empirical study. Arxiv preprint arXiv: 1106.0257, (2011)
4. Polikar, R.: Ensemble based systems in decision making. IEEE Circuits and Systems Magazine, 6(3), 21-45 (2006)
5. Cortes, C. and Vapnik, V.: Support-Vector Networks. Machine Learning, 20(3), 273-297 (1995)
6. Shakhna-rovich, G. and Darrell, T. and Indyk, P.: Nearest-Neighbor Methods in Learning and Vision. IEEE Transactions on Neural Networks, 19(2), 377 (2008)
7. Abdi, H.: Discriminant correspondence analysis. Encyclopedia of measurement and statistics, 270-275 (2007)
8. W. Tu and S. Sun: A subject transfer framework for EEG classification. Neurocomputing, 82, 109-116 (2012)
9. Klami, A. and et al: ICANN/PASCAL2 Challenge: MEG Mind-Reading?Overview and Results. Proceedings of the 20th International Conference on Artificial Neural Networks, 3 (2011)
10. Blankertz, B. and et al: Invariant common spatial patterns: Alleviating nonstationarities in braincomputer interfacing. Advances in Neural Information Processing Systems, 20, 113-120 (2008)
11. W. Dai, Q. Yang, G. R. Xue and Y. Yu. Boosting for transfer learning. Proceedings of the 24th International Conference on Machine Learning, 193-200 (2007)
12. Jacobs, R.A. and Jordan, M.I. and Nowlan, S.J. and Hinton, G.E.: Adaptive mixtures of local experts. Neural computation, 3(1), 79-87 (1991)
13. W. Dai, Q. Yang, G. R. Xue and Y. Yu.: Boosting for transfer learning. Proceedings of the 24th International Conference on Machine Learning, 193-200 (2007)
14. Zhu, X.: Semi-supervised learning literature survey. Tech.Report 1530, Department of Computer Sciences, University of Wisconsin at Madison, Madison, WI, (2006)
15. Richard O. Duda, Peter E. Hart, David G. Stork: Unsupervised Learning and Clustering. Lecture notes on Learning Theory for Information and Signal Processing, Vienna University of Technology, 29, 30 (2006)
16. Kosala, R. and Blockeel, H.: Web mining research: A survey. ACM Sigkdd Explorations Newsletter, 2(1), 1-15 (2000)
17. Bates, M.: Models of natural language understanding. Proceedings of the National Academy of Sciences of the United States of America, 92(22), 9977-9982 (1995)
18. Lu, D. and Weng, Q.: A survey of image classification methods and techniques for improving classification performance. International Journal of Remote Sensing, 28(5), 823-870 (2007)