Transfer learning with part-based ensembles

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

Volumn 7872 LNCS, Issue , 2013, Pages 271-282

  Sun, Shiliang ^a   Xu, Zhijie ^a   Yang, Mo ^a  

^a EAST CHINA NORMAL UNIVERSITY   (China)

Author keywords

Multi source learning; Part based model; Support vector machine; Transfer learning

Indexed keywords

INFORMATION TRANSFERS; MACHINE LEARNING RESEARCH; MULTISOURCES; PARAMETER TRANSFERS; PART-BASED MODELS; SUPPORT VECTOR MACHINE (SVMS); TRANSFER INFORMATION; TRANSFER LEARNING;

ARTIFICIAL INTELLIGENCE; COMPUTER SCIENCE; COMPUTERS;

SUPPORT VECTOR MACHINES;

EID: 84892923727     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-38067-9_24     Document Type: Conference Paper

References (19)

1. Caruana, R.: Multitask Learning. Machine Learning 28, 41-75 (1997)
2. Dai, W., Yang, Q., Xue, G.R., Yu, Y.: Boosting for transfer learning. In: Proceedings of the 24th International Conference on Machine Learning, pp. 193-200 (2007)
3. Evgeniou, T., Pontil, M.: Regularized multi-task learning. In: Proceedings of the 10th International Conference on Knowledge Discovery and Data Mining, pp. 109-117 (2004)
4. Xu, Z., Sun, S.: An algorithm on multi-view adaboost. In: Proceedings of the 17th International Conference on Neural Information Processing, pp. 355-362 (2010)
5. Zhang, Q., Sun, S.: Multiple-view multiple-learner active learning. Pattern Recognition 43(9), 3113-3119 (2010)
6. Thrun, S., Pratt, L.: Learning to Learn. Kluwer Academic Publishers (1997)
7. Thrun, S.: Explanation-Based Neural Network Learning: A Lifelong Learning Approach. Kluwer Academic Publishers, Boston (1996)
8. Cao, B., Liu, N.N., Yang, Q.: Transfer learning for collective link prediction in multiple heterogenous domains. In: Proceedings of the 27th International Conference on Machine Learning, pp. 159-166 (2010)
9. Rohrbach, M., Stark, M., Szarvas, G., Gurevych, I., Schiele, B.: What helps where and why? Semantic relatedness for knowledge transfer. In: Proceedings of the 23rd Computer Society Conference on Computer Vision and Pattern Recognition, pp. 910-917 (2010)
10. Wu, P., Dietterich, T.G.: Improving SVM accuracy by training on auxiliary data sources. In: Proceedings of the 21st International Conference on Machine Learning, pp. 871-878 (2010)
11. Davis, J., Domingos, P.: Deep transfer via second-order Markov logic. In: Proceedings of the 26th International Conference on Machine Learning, pp. 217-224 (2009)
12. Ross, D.A., Zemel, R.S.: Learning parts-based representations of data. Journal of Machine Learning Research 7, 2369-2397 (2006)
13. Bar-Hillel, A., Hertz, T., Weinshall, D.: Object class recognition by boosting a part-based model. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Recognition, pp. 702-709 (2005)
14. Perkins, D.N., Salomon, G.: Transfer of learning. The Journal of International Encyclopedia of Education 11, 6452-6457 (1992)
15. Crammer, K., Kearns, M., Wortman, J.: Learning from multiple sources. Journal of Machine Learning Research 9, 1757-1774 (2008)
16. Luo, P., Zhuang, F., Xiong, Y., He, Q.: Transfer learning from multiple source domains via consensus regularization. In: Proceedings of the 17th ACM Conference on Information and Knowledge Management, pp. 103-112 (2008)
17. Xu, Z., Sun, S.: Part-based transfer learning. In: Proceedings of the 8th Internaional Symposium on Neural Networks, pp. 434-441 (2011)
18. Lima, N.H.C., Neto, A.D.D., Melo, J.D.: Creating an ensemble of diverse support vector machines using Adaboost. In: Proceedings of the International Joint Conference on Neural Networks, pp. 2342-2346 (2009)
19. Shawe-Taylor, J., Sun, S.: A review of optimization methodologies in support vector machines. Neurocomputing 74(17), 3609-3618 (2011)