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Article . 2024
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Исследование и перспективы обучения моделей искусственного интеллекта и машинного обучения на больших данных: кластеризация и ее роль в эффективной обработке информации

descriptionPublicationkeyboard_double_arrow_right Article 01 Jan 2024 Russian
Authors: Saveliev, M.; Saranciuc, D.I.;

Исследование и перспективы обучения моделей искусственного интеллекта и машинного обучения на больших данных: кластеризация и ее роль в эффективной обработке информации

Abstract

Данная статья нацелена на исследование и анализ актуальных аспектов обучения моделей искусственного интеллекта ИИ и машинного обучения МО на больших данных, с углубленным фокусом на роли кластеризации в процессе эффективной обработки информации.

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Keywords

искусственный интеллект, алгоритмы кластеризации, анализ данных, большие данные, эффективная обработка информации, взаимосвязь данных, кластеризация, машинное обучение

  • BIP!
    Impact byBIP!
    selected citations
    These citations are derived from selected sources.
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    		 0
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    		 Average
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    		 Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    		 Average
Powered by OpenAIRE graph
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Green