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Titlebook: Data Mining and Big Data; 8th International Co Ying Tan,Yuhui Shi Conference proceedings 2024 The Editor(s) (if applicable) and The Author(

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樓主: Jejunum
51#
發(fā)表于 2025-3-30 10:34:03 | 只看該作者
52#
發(fā)表于 2025-3-30 14:39:11 | 只看該作者
Collective Bargaining in Labour Law Regimeso-frequency analogue sentence relationship. Based on the context compatibility algorithm to study on the cross-lingual text searching, we designed the preliminary experiment and carried it out with some distinction effect.
53#
發(fā)表于 2025-3-30 16:53:00 | 只看該作者
https://doi.org/10.1007/978-3-030-16977-0approach achieves a significant improvement in macro-F1 compared to the direct distillation methods. Importantly, it exhibits commendable performance when trained on few-shot datasets and compact models.
54#
發(fā)表于 2025-3-30 22:39:56 | 只看該作者
55#
發(fā)表于 2025-3-31 03:56:53 | 只看該作者
Macroeconomic Policy and Collective Actionclosely resemble the original abstracts without being detected by the plagiarism detector Turnitin in most cases. This implies that GPT-4 can produce logical and reasonable abstracts of articles on its own. Also, we conducted a cross-temporal analysis of GPT-4’s effectiveness and observed continuous
56#
發(fā)表于 2025-3-31 07:22:33 | 只看該作者
57#
發(fā)表于 2025-3-31 12:01:53 | 只看該作者
Comments on P. G. Hare and D. T. Ulphrithms, optimized by our framework, achieves lower error rates and requires fewer features. Consequently, we posit that reinforcement learning can offer novel methods and ideas for the application of evolutionary computing in feature selection.
58#
發(fā)表于 2025-3-31 13:47:54 | 只看該作者
59#
發(fā)表于 2025-3-31 17:31:25 | 只看該作者
Collective Bargaining in Labour Law Regimeseters and more straightforward architectures, surpass the esteemed GPT-3.5 and GPT-4 models in predictive metrics of accuracy and f1. All fine-tuned models are publicly available on the huggingface platform (.).
60#
發(fā)表于 2025-4-1 01:13:11 | 只看該作者
Data Analytics Methods in Human Resource Demand Forecastingnterprise personnel, and the feasibility of the multiple regression model is verified. At the same time, the BP neural network algorithm is described in detail, and an example is given to compare the forecasting results of multiple linear regression method and BP neural network algorithm.
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