On the Relationship between the Knowledge Model and the Problem of Pattern Recognition
- Authors: Polyakov O.M.1, Rudnitskiy S.B.1
-
Affiliations:
- Saint-Petersburg State University of Aerospace Instrumentation
- Issue: No 3 (2023)
- Pages: 16-22
- Section: Knowledge Representation
- URL: https://journals.rcsi.science/2071-8594/article/view/270338
- DOI: https://doi.org/10.14357/20718594230302
- ID: 270338
Cite item
Full Text
Abstract
The article is devoted to the problem of pattern decomposition in solving the problem of pattern recognition. The problem of pattern decomposition is considered regardless of the recognition algorithms used. The only requirement is that the pattern recognition problem has a classical formulation. The article shows that without reference to the knowledge model, the decomposition of pattern cannot be performed within the framework of the recognition task itself, since it leads to a revision of the recognition task itself. In those cases, when the pattern recognition problem is preserved during decomposition, it may change in such a way that its solution in the decomposed form is not identical to the solution of the original pattern recognition problem.
Full Text

About the authors
Oleg M. Polyakov
Saint-Petersburg State University of Aerospace Instrumentation
Author for correspondence.
Email: road.dust.spb@gmail.com
Candidate of technical sciences. Docent
Russian Federation, Saint-PetersburgSergey B. Rudnitskiy
Saint-Petersburg State University of Aerospace Instrumentation
Email: sbr@spiiras.ru
Doctor of Technical Sciences, Professor. Senior Researcher
Russian Federation, Saint-PetersburgReferences
- The RapidMiner Platform // Electronic resource. URL: https://rapidminer.com/ (accessed 04.07.2023).
- Fomin Ya. A., Tarlovsky G.R. Statisticheskaya teoriya raspoznavaniya obrazov [Statistical theory of pattern recognition]. Moscow: Radio and Communication, 1986.
- Potapov A. A., Pakhomov A.A., S. Nikitin.A. Noveyshiye metody obrabotki izobrazheniy [The latest methods of image processing]. Moscow: Fizmatlit, 2008.
- Shapiro L., Stockman J. Komp'yuternoye zreniye. [Computer vision]. Moscow: Binom. Laboratory of Knowledge, 2006.
- Merkov A. B. Raspoznavaniye obrazov. Vvedeniye v metody statisticheskogo obucheniya [Pattern recognition. Introduction to methods of statistical training]. Moscow: Editorial URSS, 2011.
- Nicolas J. First Order Logic Formalization for Functional, Multivalued and Mutual Dependencies. ACM SIGMOD Conf, 1978. P. 40-46.
- Rissanen J. Independent Components of Relations. ACM TODS 2:4, December, 1977. P. 317-325.
- Fagin R. Multivalued Dependencies and a New Normal Form for Relational Databases. ACM TODS 2:3, September. 1977. P. 262-278.
- R-lingvistika [R-linguistics]. // Electronic resource. URL: https://roaddust.ru/?cat=36 / (accessed 22.04.2023).
- Polyakov O.М. Linguistic data model for natural languages and artificial intelligence. Part 1. Categorization // DISCOURSE. 2019. V. 5. No 4. P.102–114.
- Polyakov O. M. Linguistic Data Model for Natural Languages and Artificial Intelligence. Part 2. Identification. DISCOURSE. 2019. V. 5.No. 5. P. 99-113.
- Polyakov O. M. Linguistic Data Model for Natural Languages and Artificial Intelligence. Part 3. Recognition. DISCOURSE. 2019. V. 5. No. 6. P. 132-143.
