Abstract
A novel definition of syntactic distance between structural symbolic descriptions is proposed. It is based on a probabilistic interpretation of the canonical matching predicate. By means of this distance measure it is possible to cope with the problem of matching noise affected descriptions or imprecise rules. Furthermore, an extension of the syntactic distance which manages incomplete descriptions is presented. Finally, the application of the syntactic distance to the problem of classifying digitized office documents by using their page layout description is shown.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
References
SANFELIU, A., & FU, K. S. (1983). A distance measure between attributed relational graphs for Pattern recognition. IEEE Trans. Syst., Man, and Cybern., vol.SMC-13,353–362.
WONG, A. K. C., & YOU, M. (1985). Entropy and distance of random graphs with application to structural pattern recognition. IEEE Trans. Pattern Anal. Machine Intell., vol.PAMI-7,599–609.
ESHERA, M.A., & FU, K.S. (1984). A graph distance measure for image analysis. IEEE Trans. Syst., Man, and Cybern., vol.SMC-14,398–408.
MICHALSKI, R. S., MOZETIC, I., HONG, J., & LAVRAC, N. (1986). The AQ15 Inductive Learning System: An Overview and Experiments, Intelligent Systems Group, Department of Computer Science, University of Illinois, Urbana, IL.
KODRATOFF, Y., & TECUCI, G. (1988). Learning Based on Conceptual Distance. IEEE Trans. Pattern Anal. Machine Intell., vol.PAMI-10,897–909.
MICHALSKI, R. S. (1980). Pattern Recognition as Rule-Guided Inductive Inference. IEEE Trans. on Pattern Anal. Machine Intell., vol.PAMI-2,349–361.
LARSON, J.B. (1977). Inductive Inference in the Variable Valued Predicate Logic System VL21:Methodology and Computer implementation. Doctoral dissertation, Dept. of Computer Science, University of Illinois, Urbana, Illinois.
WINSTON, P.H. (1984). Artificial Intelligence (2nd ed.). Addison-Wesley, Reading, Mass., 391–414.
SHAPIRO, L.G., & HARALICK, R.M. (1985). A Metric for Comparing Relational Descriptions. IEEE Trans. on Pattern Anal. Machine Intell., vol.PAMI-2,90–94.
ESPOSITO, F., MALERBA, D., SEMERARO G., ANNESE, E., & SCAFURO, G. (1990). Empirical learning methods for digitized document recognition: an integrated approach to inductive generalization. Proceedings of the sixth IEEE Conference on Artificial Intell. Applications, Santa Barbara, CA, 37–45.
ESPOSITO,F.,MALERBA,D.,SEMERARO,G.,ANNESE,E.,SCAFURO,G.(1990). An experimental page lay outrecognition system for office document automatic classification: an integrated approach for inductive generalization. Proc. of the 10th IEEE Int. Conf. on Pattern Recognition, Atlantic City, NJ, 557–562.
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 1991 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Esposito, F., Malerba, D., Semeraro, G. (1991). A distance measure for decision making in uncertain domains. In: Bouchon-Meunier, B., Yager, R.R., Zadeh, L.A. (eds) Uncertainty in Knowledge Bases. IPMU 1990. Lecture Notes in Computer Science, vol 521. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0028141
Download citation
DOI: https://doi.org/10.1007/BFb0028141
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-54346-6
Online ISBN: 978-3-540-47580-4
eBook Packages: Springer Book Archive