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Computational Intelligence and Neuroscience
Volume 2016, Article ID 7046563, 8 pages
http://dx.doi.org/10.1155/2016/7046563
Research Article

Generalization Bounds Derived IPM-Based Regularization for Domain Adaptation

College of Command Information System, PLA University of Science and Technology, Nanjing 210007, China

Received 24 April 2015; Revised 24 July 2015; Accepted 24 August 2015

Academic Editor: Hasan Ayaz

Copyright © 2016 Juan Meng et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. V. N. Vapnik, Statistical Learning Theory, Wiley, 1998.
  2. P. Wu and T. G. Dietterich, “Improving SVM accuracy by training on auxiliary data sources,” in Proceedings of the 21st International Conference on Machine Learning (ICML '04), pp. 871–878, ACM, July 2004. View at Scopus
  3. J. Blitzer, R. McDonald, and F. Pereira, “Domain adaptation with structural correspondence learning,” in Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP '06), pp. 120–128, Association for Computational Linguistics, July 2006. View at Scopus
  4. S. Ben-David, J. Blitzer, K. Crammer, A. Kulesza, F. Pereira, and J. W. Vaughan, “A theory of learning from different domains,” Machine Learning, vol. 79, no. 1-2, pp. 151–175, 2010. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  5. Y. Mansour, M. Mohri, and A. Rostamizadeh, “Domain adaptation with multiple sources,” in Proceedings of the 22nd Annual Conference on Neural Information Processing Systems (NIPS '08), pp. 1041–1048, December 2009. View at Scopus
  6. J. Blitzer, K. Crammer, A. Kulesza, F. Pereira, and J. Wortman, “Learning bounds for domain adaptation,” in Advances in Neural Information Processing Systems, pp. 129–136, 2008. View at Google Scholar
  7. C. Zhang, L. Zhang, and J. Ye, “Generalization bounds for domain adaptation,” in Advances in Neural Information Processing Systems, pp. 3320–3328, MIT Press, 2012. View at Google Scholar
  8. A. Müller, “Integral probability metrics and their generating classes of functions,” Advances in Applied Probability, vol. 29, no. 2, pp. 429–443, 1997. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  9. S. Ben-David, J. Blitzer, K. Crammer et al., “Analysis of representations for domain adaptation,” in Advances in Neural Information Processing Systems, vol. 19, p. 137, 2007. View at Google Scholar
  10. D. Kifer, S. Ben-David, and J. Gehrke, “Detecting change in data streams,” in Proceedings of the 30th International Conference on Very Large Data Bases, vol. 30, pp. 180–191, VLDB Endowment, 2004.
  11. L. Duan, I. W. Tsang, D. Xu, and T.-S. Chua, “Domain adaptation from multiple sources via auxiliary classifiers,” in Proceedings of the 26th International Conference On Machine Learning, (ICML '09), pp. 289–296, ACM, June 2009. View at Scopus
  12. L. Duan, D. Xu, and I. W.-H. Tsang, “Domain adaptation from multiple sources: a domain-dependent regularization approach,” IEEE Transactions on Neural Networks and Learning Systems, vol. 23, no. 3, pp. 504–518, 2012. View at Publisher · View at Google Scholar · View at Scopus
  13. S. Mendelson, “A few notes on statistical learning theory,” in Advanced Lectures on Machine Learning, vol. 2600 of Lecture Notes in Computer Science, pp. 1–40, Springer, Berlin, Germany, 2003. View at Publisher · View at Google Scholar
  14. B. K. Sriperumbudur, K. Fukumizu, A. Gretton, B. Schölkopf, and G. R. Lanckriet, “On the empirical estimation of integral probability metrics,” Electronic Journal of Statistics, vol. 6, pp. 1550–1599, 2012. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  15. K. M. Borgwardt, A. Gretton, M. J. Rasch, H.-P. Kriegel, B. Schölkopf, and A. J. Smola, “Integrating structured biological data by kernel maximum mean discrepancy,” Bioinformatics, vol. 22, no. 14, pp. e49–e57, 2006. View at Publisher · View at Google Scholar · View at Scopus
  16. Y. Yang and C. G. Chute, “A linear least squares fit mapping method for information retrieval from natural language texts,” in Proceedings of the 14th Conference on Computational Linguistics, vol. 2, pp. 447–453, 1992.
  17. R. Rifkin, G. Yeo, and T. Poggio, “Regularized least-squares classification,” in NATO Science Series, III: Computer and Systems Sciences, vol. 190, pp. 131–154, IOS Press, 2003. View at Google Scholar
  18. J. A. K. Suykens and J. Vandewalle, “Least squares support vector machine classifiers,” Neural Processing Letters, vol. 9, no. 3, pp. 293–300, 1999. View at Publisher · View at Google Scholar · View at Scopus