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  • SEN WANG (a1), QINGXIANG FANG (a2) and JUN-E FENG (a1)

We solve the problem of concept learning using a semi-tensor product method. All possible hypotheses are expressed under the framework of a semi-tensor product. An algorithm is raised to derive the version space. In some cases, the new approach improves the efficiency compared to the previous approach.

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[1] Cheng, D., “Semi-tensor product of matrices and its application to Morgen’s problem”, Sci. China Inf. Sci. 44 (2001) 195212; doi:10.1007/BF02714570.
[2] Cheng, D. and Qi, H., “Controllability and observability of Boolean control networks”, Automatica 45 (2009) 16591667; doi:10.1016/j.automatica.2009.03.006.
[3] Cheng, D. and Qi, H., “A linear representation of dynamics of Boolean networks”, IEEE Trans. Automat. Control 55(10) (2010) 22512258; doi:10.1109/TAC.2010.2043294.
[4] Cheng, D., Qi, H., Li, Z. and Liu, J., “Stability and stabilization of Boolean networks”, Internat. J. Robust Nonlinear Control 21 (2011) 134156; doi:10.1002/rnc.1581.
[5] Haussler, D., “Quantifying inductive bias: AI learning algorithms and Valiant’s learning framework”, Artificial Intelligence 36 (1988) 177221; doi:10.1016/0004-3702(88)90002-1.
[6] Mitchell, T. M., Machine learning (McGraw-Hill, Boston, MA, 1997);
[7] Mitchell, T. M., “Version spaces: a candidate elimination approach to rule learning”, in: IJCAI’77 Proceedings of the 5th international joint conference on Artificial intelligence - Volume 1 (Morgan Kaufmann Publishers Inc., San Francisco, CA, 1977) 305310;
[8] Zhao, Y., Qi, H. and Cheng, D., “Input-state incidence matrix of Boolean control networks and its applications”, Systems Control Lett. 59 (2010) 767774; doi:10.1016/j.sysconle.2010.09.002.
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The ANZIAM Journal
  • ISSN: 1446-1811
  • EISSN: 1446-8735
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