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Titlebook: Data Analytics and Management in Data Intensive Domains; 23rd International C Alexei Pozanenko,Sergey Stupnikov,Nadezhda Kiselyo Conference

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41#
發(fā)表于 2025-3-28 17:28:15 | 只看該作者
Adebayo Fayoyin,Emmanuel K. Ngwainmbies not require a large amount of labeled data, which saves time and effort for data labeling and therefore can be applied in low- and mid-resource settings. The source code is publicly available and can be used for different research purposes.
42#
發(fā)表于 2025-3-28 20:57:20 | 只看該作者
43#
發(fā)表于 2025-3-28 22:54:24 | 只看該作者
44#
發(fā)表于 2025-3-29 03:26:30 | 只看該作者
45#
發(fā)表于 2025-3-29 10:32:43 | 只看該作者
A System for?Information Extraction from?Scientific Texts in?Russianes not require a large amount of labeled data, which saves time and effort for data labeling and therefore can be applied in low- and mid-resource settings. The source code is publicly available and can be used for different research purposes.
46#
發(fā)表于 2025-3-29 13:19:14 | 只看該作者
47#
發(fā)表于 2025-3-29 15:54:28 | 只看該作者
Conference proceedings 2022 DAMDID/RCDL 2021, held in Moscow, Russia, in October 2021*..The 16 revised full papers were carefully reviewed and selected from 61 submissions. The papers are organized in the following topical sections:?problem solving infrastructures, experiment organization, and machine learning applications; d
48#
發(fā)表于 2025-3-29 22:18:29 | 只看該作者
49#
發(fā)表于 2025-3-30 01:51:28 | 只看該作者
https://doi.org/10.1007/978-3-642-34568-5gration of different open source tools for running research experiments. We implement our approach in a prototype open source MLDev software package and evaluate it in a series of experiments yielding promising results. Comparison with other state-of-the-art tools signifies novelty of our approach.
50#
發(fā)表于 2025-3-30 04:53:07 | 只看該作者
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