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News
July 9, 2026
HSE Economists Use Search Queries to Forecast Birth Rates
Researchers from the HSE Faculty of Economic Sciences have shown that the accuracy of birth rate forecasts for Russia can be improved by almost 50% by incorporating the dynamics of online search queries related to pregnancy and childbirth into forecasting models. In the best-performing models, the forecasting error fell from 4.6% to 3.2%. The findings have been published in Populations and Economics.
July 8, 2026
HSE Researchers Discover Who Eats Out in Russia-And Why
Around one-third of Russians (31.3%) rarely eat out or buy ready-made meals. The core group of active consumers—those who eat out or purchase prepared food almost every day or several times a week—accounts for only about 9% of the population. These are the findings of a study conducted by the HSE Institute for Social Policy. According to the researchers eating out is no longer a marker of high social status in Russia.
July 8, 2026
HSE University and RREDA Join Forces to Support 2026 Renewable Energy of the Planet Competition
HSE University and the Russia Renewable Energy Development Association (RREDA) have signed a partnership and information cooperation agreement to support Renewable Energy of the Planet—2026, a national competition with international participation for students and early-career researchers. Applications are open on the competition's website until September 20, 2026.

 

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The Feature Selection Method Based on a Probabilistic Approach and a Cross-Entropy Metric for the Image Recognition Problem

Scientific and Technical Information Processing. 2021. Vol. 48. No. 6. P. 430–435.
Dubnov Y. A.

This paper considers the problem of feature selection in the classification problem. A method for selecting informative features based on a probabilistic approach and cross-entropy metrics is proposed. Several variants of the information criterion for selecting features for a binary classification problem are considered, as well as its generalization to the case of a multiclass problem. Demonstration examples of the proposed method for the task of image recognition from the mnist collection are given.

Research target: Computer Science Mathematics
Language: English
Full text
DOI
Keywords: cross-entropyclassificationfeature selection
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