首页/数据集/CPU time and iterations consumed by each method in Fig 4.

CPU time and iterations consumed by each method in Fig 4.

NIAID Data Ecosystem2026-03-10 收录

算法评估

效率分析

数据链接：

https://figshare.com/articles/dataset/CPU_time_and_iterations_consumed_by_each_method_in_Fig_4_/5833209 数据链接链接失效反馈

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CPU time and iterations consumed by each method in Fig 4.

应用场景：

创建时间：

2018-01-30

时间序列变点检测

算法评估

Noise: different levels of noise were added to the original time series. The numbers in the second column denote the strength of noise, e.g., 50 refers to added noise with a variance of 50% of the ori

Figshare2022-03-11 更新60

Results for different basic classifiers (mean±SD) by using varied numbers of supplementary training data, trained and tested in 10-fold cross-validation on the plant dataset.

植物分类识别

算法评估

Results for different basic classifiers (mean±SD) by using varied numbers of supplementary training data, trained and tested in 10-fold cross-validation on the plant dataset.

Figshare2015-12-02 更新30

Fairlex: A multilingual benchmark for evaluating fairness in legal text processing

自然语言处理

算法评估

We present a benchmark suite of four datasets for evaluating the fairness of pre-trained legal language models and the techniques used to fine-tune them for downstream tasks. Our benchmarks cover four

Zenodo2022-03-02 更新20

Results of runtime comparison for 40 pairs of images on the TNO dataset. (Unit: seconds).

多模态图像

算法评估

Results of runtime comparison for 40 pairs of images on the TNO dataset. (Unit: seconds).

Figshare2025-07-07 更新50

AUROC values from different algorithms for variables in microarray data.

微阵列数据

算法评估

AUROC values from different algorithms for variables in microarray data.

NIAID Data Ecosystem30