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The BRAIN Learning Algorithm

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Neural Nets WIRN VIETRI-98

Part of the book series: Perspectives in Neural Computing ((PERSPECT.NEURAL))

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Abstract

This paper describes a new learning algorithm (BRAIN), inferring DNF Boolean formulae from examples. The formula terms are computed in an iterative way, by identifying from the training set a relevance coefficient for each attribute. Results on Splice-Junction Gene Sequences and Breast Cancer machine learning data sets are reported.

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References

  1. C. M. Bishop, Neural networks for pattern recognition, Clarendon, Oxford, 1996.

    MATH  Google Scholar 

  2. A. Blumer, A. Ehrenfeucht, D. Haussier, “Occam’s razor”, Inf. Proc. Lett., 24, 1987, pp. 377–380.

    Article  MATH  Google Scholar 

  3. G. Cestnik, I. Konenenko, I. Bratko, “Assistant-86”, In I. Bratko & N. Lavrac ed.s Progress in Machine Learning, 31–45, Sigma Press, 1987.

    Google Scholar 

  4. T. H. Cormen, C. H. Leiserson, R. L. Rivest, Introduction to algorithms, The MIT Press, Cambridge, 1990.

    MATH  Google Scholar 

  5. T. G. Dietterich, J. W. Shaving, ed. s, Readings in machine learning, Morgan Kaufmann, San Mateo, 1990.

    Google Scholar 

  6. D. Haussier, “Quantifying Inductive Bias: AI learning algorithms and Valiant’s learning framework”, Artificial Intelligence, 36, 1988, pp. 177–222.

    Article  MathSciNet  Google Scholar 

  7. D. S. Johnson, “Approximation algorithms for combinatorial problems”, J. Comput. Syst. Sci., 9 1974 pp. 256–278.

    Google Scholar 

  8. M. Kearns, M. Li, L. Pitt, L. Valiant, “On the Learnability of Boolean formulae”, Proc. 9th Annual ACM Sym. on Theory of computing, 1987, pp. 285–295.

    Google Scholar 

  9. R. S. Michalski, “A theory and methodology of inductive learning”, Artificial Intelligence. 20 1983 pp. 111–116.

    Article  MathSciNet  Google Scholar 

  10. R. S. Michalsky, I. Mozetic, J. Hong, N. Lavrac, “The multi-purpose incremental learning system AQ15” Proc. 5thNCAI, 1041–1045, PA, Morgan Kaufmann, 1986.

    Google Scholar 

  11. T. M. Mitchell, “Generalization as search”, Art. Int, 18, 1982, pp. 203–226.

    Article  Google Scholar 

  12. M. O. Noordewier, G. G. Towell, J. W. Shavlik, “Training knowledge-based neural networks to recognize genes in DNA sequences”. Advances in Neural Information Processing Systems 3, Morgan Kaufmann, 1991.

    Google Scholar 

  13. M. Tan, L. Eshelman, “Using weighted networks to represent classification knowledge in noisy domains”, Proc. 5th Int. Conf. Mac. Le., 121–134, Ann Arbor, 1988.

    Google Scholar 

  14. G. G. Towell, J. W. Shavlik, M. W. Craven, “Constructive induction in knowledge-based neural networks”, Proc. 8th Int. Mac. Le. Work. Morgan Kaufmann, 1991.

    Google Scholar 

  15. G. G. Towell, J. W. Shavlik, “Interpretation of artificial neural networks”, Advances in Neural Information Processing Systems 4, Morgan Kaufmann, 1992.

    Google Scholar 

  16. S. Weiss, I. Kapouless, “An empirical comparison of pattern recognition methods”, Proc. 11th IJCAI, Morgan Kaufmann, Detroit, 1989.

    Google Scholar 

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© 1999 Springer-Verlag London Limited

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Rampone, S. (1999). The BRAIN Learning Algorithm. In: Marinaro, M., Tagliaferri, R. (eds) Neural Nets WIRN VIETRI-98. Perspectives in Neural Computing. Springer, London. https://doi.org/10.1007/978-1-4471-0811-5_13

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  • DOI: https://doi.org/10.1007/978-1-4471-0811-5_13

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-4471-1208-2

  • Online ISBN: 978-1-4471-0811-5

  • eBook Packages: Springer Book Archive

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