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A Structural Learning Algorithm and Its Application to Predictive Toxicology Evaluation

  • Pasquale Foggia
  • Michele Petretta
  • Francesco Tufano
  • Mario Vento
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3704)

Abstract

A common problem encountered in structural pattern recognition is the difficulty of constructing classification models or rules from a set of examples, due to the complexity of the structures needed to represent the patterns. In this paper we present an extension of a method for structural learning applied to predictive toxicology evaluation.

Keywords

Logic Program Logic Programming Inductive Logic Programming Heuristic Function Inductive Learn 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Eshera, M.A., Fu, K.S.: An image understanding system using attributed symbolic representation and inexact graph matching. IEEE Trans. Pattern Analysis and Machine Intelligence PAMI-8(5), 604–617 (1986)CrossRefGoogle Scholar
  2. 2.
    Hinton, G.E.: Mapping Part-Whole Hierarchies into Connectionist Networks. Artificial Intelligence 46, 47–75 (1990)CrossRefGoogle Scholar
  3. 3.
    Winston, P.H.: Learning Structural Descriptions from Examples, Technical Report MAC-TR-76, Dept. of Electrical Engineering and Computer Science, MIT (1970)Google Scholar
  4. 4.
    Foggia, P., Genna, R., Vento, M.: Symbolic vs Connectionist Learning: an Experimental Comparison in a Structured Domain. IEEE Transactions on Knowledge and Data Engineering 13-2, 176–195 (2001)CrossRefGoogle Scholar
  5. 5.
    Muggleton, S.: Inductive Logic Programming. New Generation Computing 8(4), 295–318 (1991)zbMATHCrossRefGoogle Scholar
  6. 6.
    Quinlan, J.R.: Learning logical definitions from relations. Machine Learning 5(3), 239–266 (1990)Google Scholar
  7. 7.
    Bahler, D., Bristol, D.: The induction of rules for predicting chemical cancirogenesis. In: Proceedings of 26th Hawaii International Conference on System Science. IEEE Computer Society Press, Los Alamitos (1993)Google Scholar
  8. 8.
    Muggleton, S.: Inverse Entailment and Progol. New Generation Computing Journal 13, 245–286 (1995)CrossRefGoogle Scholar
  9. 9.
    Cook, D., Holder, L.B.: Substructure Discovery Using Minimum Description Length and Background Knowledge. Journal of Artificial Intelligence Research 1, 231–255 (1994)Google Scholar
  10. 10.
    Gonzales, J., Holder, L., Cook, D.: Application of Graph-Based Concept Learning to the Predictive Toxicology Domain. In: Proceedings of the Florida Artificial Intelligence Research Symposium, pp. 377–381 (2001)Google Scholar
  11. 11.
    Cordella, L.P., Foggia, P., Genna, R., Vento, M.: Prototyping Structural Descriptions: An Inductive Learning Approach. In: Amin, A., Pudil, P., Dori, D. (eds.) SPR 1998 and SSPR 1998. LNCS, vol. 1451, pp. 339–348. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  12. 12.
    Michalski, R.S.: Pattern Recognition as Rule-Guided Inductive Inference. IEEE Trans. Pattern Analysis and Machine Intelligence 2(4), 349–361 (1980)zbMATHCrossRefGoogle Scholar
  13. 13.
    Michalski, R.S.: A Theory and Methodology of Inductive Learning. In: Michalski, R.S., Carbonell, J.S., Mitchell, T.M. (eds.) Machine Learning: An Artificial Intelligence Approach, ch. 4, vol. 1, pp. 83–133 (1983)Google Scholar
  14. 14.
    Dietterich, T.G., Michalski, R.S.: A Comparative Review of Selected Methods for Learning from Examples. In: Michalski, R.S., Carbonell, J.S., Mitchell, T.M. (eds.) Machine Learning: An Artificial Intelligence Approach, ch. 3, vol. 1, pp. 41–82 (1983)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Pasquale Foggia
    • 2
  • Michele Petretta
    • 1
  • Francesco Tufano
    • 1
  • Mario Vento
    • 1
  1. 1.Dipartimento di Ingegneria dell’Informazione ed Ingegneria ElettricaUniversità di SalernoFisciano (SA)Italy
  2. 2.Dipartimento di Informatica e SistemisticaUniversità di Napoli “Federico II”NapoliItaly

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