Abstract
This chapter describes a new way of pattern interpretation aimed at the automatic semantic categorization and image content understanding. Such an understanding is based on the linguistic theories of pattern classification and is aimed at facilitating the content analysis of some classes of medical and economical patterns. The approach presented in this chapter shows great opportunities for the automatic disease interpretation in some analyzed structures, and for supporting information management using the grammar approach. The interpretation is based on cognitive resonance processes which imitate the psychological processes of understanding registered patterns which take place in the brain of a human beings. Cognitive and thinking processes taking place in the human brain have become the basis for defining classes of cognitive categorization systems designed for the in-depth, meaning-based interpretation and analysis of data. This type of an analysis is only possible thanks to applying interpretation and reasoning processes usually taking place in the human brain in a system. In addition, this type of an analysis is made possible by the use of linguistic algorithms for describing, analyzing and interpreting data in computer systems. Algorithms of this type support a meaning-based analysis of data which leads to understanding the semantic content of the analyzed data and to attempts at making forecasts with regard to the analyzed information.
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Ogiela, L. (2010). Cognitive Informatics in Automatic Pattern Understanding and Cognitive Information Systems. In: Wang, Y., Zhang, D., Kinsner, W. (eds) Advances in Cognitive Informatics and Cognitive Computing. Studies in Computational Intelligence, vol 323. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16083-7_11
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