title: Ð Ð°Ð·ÑÐ°Ð±Ð¾ÑÐºÐ° Ð¸ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ðµ Ð½Ð¾Ð²ÑÑ Ð¼Ð¾Ð´ÐµÐ»ÐµÐ¹ Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð½Ð° Ð¾ÑÐ½Ð¾Ð²Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ ÑÐ´ÐµÑ ÑÐµÐ³ÑÐµÑÑÐ¸Ð¸ ÐÐ°Ð´Ð°ÑÐ°Ñ-Ð£Ð¾ÑÑÐ¾Ð½Ð°
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð±Ð°ÐºÐ°Ð»Ð°Ð²ÑÐ°
titleType: Subtitle
title: Research and developement of new machine learning models based on Nadaraya-Watson kernel regression
Creators: name: ÐÐ¸ÑÐ¿Ð¸ÑÐµÐ½ÐºÐ¾, Ð¡ÑÐ°Ð½Ð¸ÑÐ»Ð°Ð²
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2022
Subjects: Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
Ð¿Ð¾Ð»Ð½Ð¾ÑÐ²ÑÐ·Ð½Ð°Ñ Ð½ÐµÐ¹ÑÐ¾ÑÐµÑÑ
Ð¿ÑÐµÐ´Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
ÑÐ´ÐµÑÐ½Ð°Ñ ÑÐµÐ³ÑÐµÑÑÐ¸Ñ
machine learning
dense neural network
pretraining
kernel regression

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description: The subject of the graduate qualification work is Â«Research and developement of new machine learning models based on Nadaraya-Watson kernel regressionÂ». The purpose of the work is developing of the universal regressor. During the research several tasks were solved: Developing the universal regressor's architecture. Starting point of the architecture problem is the Nadaraya-Watson kernel regression. The model must be a regressor which doesn't need to be trained on the incoming data.Developing the data generation algorithm. The universal regressor should be pretrained using this algorithm. Considering different distributions of the training and testing data. Study the dependence of the kernel's structure, obtained on different features distributions. Comparing the universal regressor with other regression algorithms: the Nadaraya-Watson kernel regression, Dense neural network, k nearest neighbours, SVR and GBM. Various numerical experiments show that the proposed model outperforms its prototype and comparable with other models. There is the advantage of the developed universal regressor with known features distribution.