Abstract
Automatic inference is one of the main problems that syntactic and structural pattern recognition must solve for successful applications. Neural networks are artificial intelligence tools which already support automatic inference for successful applications of statistical pattern recognition. In this paper, we suggest that neural networks, and specifically Cascade-Correlation, can be used for automatic inference in syntactic and structural pattern recognition, as well. An extended version of a standard neuron which is able to deal with structures is presented and the Cascade-Correlation algorithm generalized to structured domains. The computational complexity of the proposed algorithm as well as experimental results obtained on problems involving logic terms are presented.
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References
S. E. Fahlman. The recurrent cascade-correlation architecture. Technical Report CMU-CS-91-100, Carnegie Mellon, 1991.
S. E. Fahlman and C. Lebiere. The cascade-correlation learning architecture. In D. S. Touretzky, editor, Advances in Neural Information Processing Systems 2, pages 524–532. San Mateo, CA: Morgan Kaufmann, 1990.
C.L. Giles, D. Chen, G.Z. Sun, H.H. Chen, Y.C. Lee, and M.W. Goudreau. Constructive learning of recurrent neural networks: Limitations of recurrent casade correlation and a simple solution. IEEE Transactions on Neural Networks, 6(4):829–836, 1995.
R. C. Gonzalez and M. G. Thomason. Syntactical Pattern Recognition. Addison-Wesley, 1978.
S. Haykin. Neural Networks: a comprehensive Foundation. IEEE Press, 1994.
S. Muggleton and L. De Raedt. Inductive login programming: Theory and methods. Journal of Logic Programming, 19,20:629–679, 1994.
T. Pavlidis. Structural Pattern Recognition. Springer-Verlag, 1977.
D. H. Rouvray. Computational Chemical Graph Theory, page 9. Nova Science Publishers: New York, 1990.
S. Russell and P. Norvig. Artificial Intelligence: a comprehensive Foundation. Prentice Hall, 1995.
R. J. Schalhoff. Pattern Recognition: Statistical, Structural and Neural Approaches. John Wiley & Sons, 1992.
A. Sperduti. Labeling RAAM. Connection Science, 6(4):429–459, 1994.
A. Sperduti. Stability properties of labeling recursive auto-associative memory. IEEE Transactions on Neural Networks, 6(6):1452–1460, 1995.
A. Sperduti, A. Starita, and C. Goller. Learning distributed representations for the classification of terms. In Proceedings of the International Joint Conference on Artificial Intelligence, pages 509–515, 1995.
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© 1996 Springer-Verlag Berlin Heidelberg
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Sperduti, A., Majidi, D., Starita, A. (1996). Extended Cascade-Correlation for syntactic and structural pattern recognition. In: Perner, P., Wang, P., Rosenfeld, A. (eds) Advances in Structural and Syntactical Pattern Recognition. SSPR 1996. Lecture Notes in Computer Science, vol 1121. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-61577-6_10
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DOI: https://doi.org/10.1007/3-540-61577-6_10
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