Abstract
A class of knowledge-based pattern recognition problems with relational and logical knowledge representation is formulated. In the first part, the learning algorithm consisting in using the learning sequence for astep-by-step evaluation, and updating of the knowledge is described. In the second part, the application of so-called uncertain variables and certainty distributions given by an expert is presented for an independent recognition problem and for recognition in a simple decision-making system. Simple examples and the results of simulations illustrating the general methods are included.
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Bubnicki, Z. Application of learning algorithms and uncertain variables in knowledge-based pattern recognition. Artif Life Robotics 5, 67–71 (2001). https://doi.org/10.1007/BF02481341
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DOI: https://doi.org/10.1007/BF02481341