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2019 | OriginalPaper | Buchkapitel

The Most Related Knowledge First: A Progressive Domain Adaptation Method

verfasst von : Yunyun Wang, Dan Zhao, Yun Li, Kejia Chen, Hui Xue

Erschienen in: Trends and Applications in Knowledge Discovery and Data Mining

Verlag: Springer International Publishing

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Abstract

In domain adaptation, how to select and transfer related knowledge is critical for learning. Inspired by the fact that human usually transfer from the more related experience to the less related one, in this paper, we propose a novel progressive domain adaptation (PDA) model, which attempts to transfer source knowledge by considering the transfer order based on relevance. Specifically, PDA transfers source instances iteratively from the most related ones to the least related ones, until all related source instances have been adopted. It is an iterative learning process, source instances adopted in each iteration are determined by a gradually annealed weight such that the later iteration will introduce more source instances. Further, a reverse classification performance is used to set the termination of iteration. Experiments on real datasets demonstrate the competiveness of PDA compared with the state-of-arts.

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Vorheriges Kapitel Adversarial Active Learning in the Presence of Weak and Malicious Oracles

Nächstes Kapitel Deep Architectures for Joint Clustering and Visualization with Self-organizing Maps

Pan, S.J., Yang, Q.: A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 22(10), 1345–1359 (2010)CrossRef

Taylor, M.E., Stone, P.: Transfer learning for reinforcement learning domains: a survey. J Mach Learn Res 10(10), 1633–1685 (2009)MathSciNetMATH

Long, M., Wang, J., Ding, G., Sun, J., Yu, P.S.: Transfer feature learning with joint distribution adaptation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2200–2207. IEEE, Sydney (2013)

Gao, J., Fan, W., Jiang, J., Han, J.: Knowledge transfer via multiple model local structure mapping. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 283–291. ACM, Las Vegas (2008)

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. ACM, Corvalis (2007)

Blitzer, J., McDonald, R., Pereira, F.: Domain adaptation with structural correspondence learning. In: Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing, pp. 120–128. Association for Computational Linguistics, Sydney (2006)

Long, M., Wang, J., Ding, G., Pan, S.J., Yu, P.S.: Adaptation regularization: a general framework for transfer learning. IEEE Trans. Knowl. Data Eng. 26(5), 1076–1089 (2014)CrossRef

Wang, J., Chen, Y., Hao, S., Feng, W., Shen, Z.: Balanced distribution adaptation for transfer learning. In: Proceedings of the IEEE International Conference on Data Mining (ICDM), pp. 1129–1134. IEEE, New Orleans (2017)

Bruzzone, L., Marconcini, M.: Domain adaptation problems: a DASVM classification technique and a circular validation strategy. IEEE Trans. Pattern Anal. Mach. Intell. 32(5), 770–787 (2010)CrossRef

10.

Kumar, M.P., Packer, B., Koller, D.: Self-paced learning for latent variable models. In: Proceedings of the 24th Annual Conference on Neural Information Processing Systems. Curran Associates Inc, Vancouver (2010)

11.

Lu, J., Meng, D., Yu, S., Lan, Z., Shan, S., Hauptmann, A.: Self-paced learning with diversity. In: Proceedings of the 28th Annual Conference on Neural Information Processing Systems, Montreal (2014)

12.

Schölkopf, B., Herbrich, R., Smola, A.J.: A generalized representer theorem. In: Helmbold, D., Williamson, B. (eds.) Computational Learning Theory 2001. LNCS, vol. 2111, pp. 416–426. Springer, Heidelberg (2001). https://doi.org/10.1007/3-540-44581-1_27CrossRef

13.

Belkin, M., Niyogi, P., Sindhwani, V.: Manifold regularization: a geometric framework for learning from labeled and unlabeled examples. J. Mach. Learn. Res. 7(1), 2399–2434 (2006)MathSciNetMATH

14.

Lu, J., Meng, D., Yu, S., Lan, Z., Shan, S., Hauptmann, A.: Supplementary materials: self-paced learning with diversity. In: Proceedings of the 28th Annual Conference on Neural Information Processing Systems, Montreal (2014)

15.

Valindria, V.V., Lavdas, I., Bai, W., et al.: Reverse classification accuracy: predicting segmentation performance in the absence of ground truth. IEEE Trans. Med. Imaging 36(8), 1597–1606 (2017)CrossRef

16.

Saenko, K., Kulis, B., Fritz, M., Darrell, T.: Adapting visual category models to new domains. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) European Conference on Computer Vision 2010. LNCS, vol. 6314, pp. 213–226. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-15561-1_16CrossRef

17.

Li, W., Duan, L., Xu, D., Tsang, I.W.: Learning with augmented features for supervised and semi-supervised heterogeneous domain adaptation. IEEE Trans. Pattern Anal. Mach. Intell. 36(6), 1134–1148 (2013)CrossRef

18.

Long, M., Wang, J., Sun, J., Yu, P.S.: Domain invariant transfer kernel learning. IEEE Trans. Knowl. Data Eng. 27(6), 1519–1532 (2015)CrossRef

19.

Long, M., Ding, G., Wang, J., Sun, J., Guo, Y., Yu, P.S.: Transfer sparse coding for robust image representation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 407–414. IEEE, Portland (2013)

Titel: The Most Related Knowledge First: A Progressive Domain Adaptation Method
verfasst von: Yunyun Wang
Dan Zhao
Yun Li
Kejia Chen
Hui Xue
Verlag: Springer International Publishing
Buch: Trends and Applications in Knowledge Discovery and Data Mining
Print ISBN: 978-3-030-26141-2

Electronic ISBN: 978-3-030-26142-9

Copyright-Jahr: 2019
DOI: https://doi.org/10.1007/978-3-030-26142-9_9

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