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F. Esposito, D. Malerba, G. Semeraro, "Classification in Noisy Environments Using a Distance Measure Between Structural Symbolic Descriptions," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 14, no. 3, pp. 390402, March, 1992.  
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@article{ 10.1109/34.120333, author = {F. Esposito and D. Malerba and G. Semeraro}, title = {Classification in Noisy Environments Using a Distance Measure Between Structural Symbolic Descriptions}, journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence}, volume = {14}, number = {3}, issn = {01628828}, year = {1992}, pages = {390402}, doi = {http://doi.ieeecomputersociety.org/10.1109/34.120333}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }  
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TY  JOUR JO  IEEE Transactions on Pattern Analysis and Machine Intelligence TI  Classification in Noisy Environments Using a Distance Measure Between Structural Symbolic Descriptions IS  3 SN  01628828 SP390 EP402 EPD  390402 A1  F. Esposito, A1  D. Malerba, A1  G. Semeraro, PY  1992 KW  attribute spaces; pattern recognition; learning systems; noisy environments; distance measure; structural symbolic descriptions; probabilistic interpretation; matching predicate; topdown evaluation; feature value; pattern model; classification; structure spaces; layoutbased document recognition; learning systems; pattern recognition; probability VL  14 JA  IEEE Transactions on Pattern Analysis and Machine Intelligence ER   
A definition of distance measure between structural descriptions that is based on a probabilistic interpretation of the matching predicate is proposed. It aims at coping with the problem of classification when noise causes both local and structural deformations. The distance measure is defined according to a topdown evaluation scheme: distance between disjunctions of conjuncts, conjunctions, and literals. At the lowest level, the similarity between a feature value in the pattern model (G) and the corresponding value in the observation (Ex) is defined as the probability of observing a greater distortion. The classification problem is approached by means of a multilayered framework in which the cases of single perfect match, no perfect match, and multiple perfect match are treated differently. A plausible solution for the problem of completing the attribute and structure spaces, based on the probabilistic approach, is also given. A comparison with other related works and an application in the domain of layoutbased document recognition are presented.
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