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Multitask Fuzzy Learning with Rule Weight

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Abstract:

In fuzzy learning system based on rule weight, certainty grade, denoted by membership function of fuzzy set, defines how close a rule to a classification. In this system, several rules can correspond to same classification. But it cannot reflect the changing while training several tasks simultaneously. In this paper, we propose multitask fuzzy learning based on error-correction, and define belonging grade to show how much a sample belongs to a rule. Experimental results demonstrate efficiency of multitask fuzzy learning, and multitask learning could help to improve learning machines prediction.

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Periodical:

Advanced Materials Research (Volumes 774-776)

Pages:

1883-1886

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.774-776.1883

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Cite this paper

Online since:

September 2013

Authors:

Zhen Xing Li, Wei Hua Li

Keywords:

Fuzzy Learning, Multitask Learning, Rule Weight

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© 2013 Trans Tech Publications Ltd. All Rights Reserved

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References

[1] Caruana, R.: Multitask Learning. Machine Learning, 28(1997): pp.41-75.

Google Scholar

[2] Argyriou, A., T. Evgeniou, and M. Pontil: Convex multi-task feature learning. Machine Learning. 73(2008): pp.243-272.

DOI: 10.1007/s10994-007-5040-8

Google Scholar

[3] Evgeniou, T., C.A. Micchelli, and M. Pontil: Learning multiple tasks with kernel methods. Journal of Machine Learning Research, 2005. 6: pp.615-637.

Google Scholar

[4] Rakotomamonjy, A., et al.: l(p)-l(q) Penalty for Sparse Linear and Sparse Multiple Kernel Multitask Learning. Ieee Transactions on Neural Networks, 8(2011): pp.1307-1320.

DOI: 10.1109/tnn.2011.2157521

Google Scholar

[5] Cavallanti, G., N. Cesa-Bianchi, and C. Gentile: Linear Algorithms for Online Multitask Classification. Journal of Machine Learning Research, 11(2010): pp.2901-2934.

Google Scholar

[6] Chapelle, O., et al.: Boosted multi-task learning. Machine Learning, 85 (2011): pp.149-173.

Google Scholar

[7] Ross, S., et al.: A Bayesian Approach for Learning and Planning in Partially Observable Markov Decision Processes. Journal of Machine Learning Research, 2011. 12.

Google Scholar

[8] Pillonetto, G., F. Dinuzzo, and G. De Nicolao: Bayesian Online Multitask Learning of Gaussian Processes. Ieee Transactions on Pattern Analysis and Machine Intelligence, 2(2010): pp.193-205.

DOI: 10.1109/tpami.2008.297

Google Scholar

[9] Ishibuchi, H. and T. Yamamoto: Rule weight specification in fuzzy rule-based classification systems. Ieee Transactions on Fuzzy Systems, 4(2005): pp.428-435.

DOI: 10.1109/tfuzz.2004.841738

Google Scholar

[10] van den Berg, J., U. Kaymak, and W.M. van den Bergh: Fuzzy classification using probability-based rule weighting. Proceedings of the 2002 Ieee International Conference on Fuzzy Systems, Vol 1 & 2(2002): pp.991-996.

DOI: 10.1109/fuzz.2002.1006639

Google Scholar