Abstract
In this paper, the concept of information modeling based on a new model of geometric transformations is considered. This concept ensures the solutions of the following tasks like pattern recognition, predicting, classification, the principal independent components selection, optimization, recovering of lost data or their consolidation and implementing the information protection and privacy methods. Neural-like structures based on the Geometric Transformations Model as universal approximator implement principles of training and self-training and base on an algorithmic or hardware performing variants using the space and time parallelization principles. Geometric Transformations Model uses a single methodological framework for various tasks and fast non-iterative study with pre-defined number of computation steps, provides repeatability of the training outcomes and the possibility to obtain satisfactory solutions for large and small training samples.
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Tkachenko, R., Izonin, I. (2019). Model and Principles for the Implementation of Neural-Like Structures Based on Geometric Data Transformations. In: Hu, Z., Petoukhov, S., Dychka, I., He, M. (eds) Advances in Computer Science for Engineering and Education. ICCSEEA 2018. Advances in Intelligent Systems and Computing, vol 754. Springer, Cham. https://doi.org/10.1007/978-3-319-91008-6_58
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