Machine learning, neural networks

On the properties of the limit set of a repeated machine learning process under feature space transformations

A. S. Veprikov^ab, A. S. Khritankov<sup>ab

a</sup> Moscow Institute of Physics and Technology (National Research University), Dolgoprudny, Moscow, Russia
^b Institute for Information Transmission Problems of the Russian Academy of Sciences (Kharkevich Institute), Moscow, Russia

Abstract: Widely used in practice the recommender systems, decision support systems, intelligent control, AI assistants in medicine, and search engines can influence users and properties of the environment in which they are employed. The process of repeated machine learning describes such systems in which continuous improvement of machine learning models is performed over time using training data obtained from the users. In this paper, we study how feature space transformations influence properties of the repeated machine learning process. In particular, we investigate the conditions under which the prediction of the asymptotic behavior of a system over time obtained in the original space can be applied to a similar system in the transformed space. The results of the research indicate the possibility of using simpler systems in spaces of lower dimensionality to study processes in more complex systems.

Keywords: machine learning, repeated machine learning, feedback loop, dynamic systems.

DOI: 10.14357/20718594250105

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