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Experimental results of different SE-ResNet architectures on Cifar.

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数据链接：

https://figshare.com/articles/dataset/Experimental_results_of_different_SE-ResNet_architectures_on_Cifar_/7925774 数据链接链接失效反馈

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资源简介：

Experimental results of different SE-ResNet architectures on Cifar.

应用场景：

创建时间：

2019-03-29

随机森林参数调优

分类器性能评估

AUC results for low p/n data. Low p/n results for prediction accuracy using AUC as the performance metric for non-cross-validation results, 10-fold cross-validation and stratified 10-fold cross-valida

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Class-specific producer’s accuracies (PA), user’s accuracies (UA), and overall accuracies (OA) (%) for the different classifiers (Manas, training sample number = 4,000).

图像分类

分类器性能评估

Class-specific producer’s accuracies (PA), user’s accuracies (UA), and overall accuracies (OA) (%) for the different classifiers (Manas, training sample number = 4,000).

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MOESM1 of Improved metagenomic analysis with Kraken 2

元基因组分析

分类器性能评估

Additional file 1: Table S1. Comparison of accuracy and computational performance. Table S2. Comparison of Kraken 2 with other classifiers, using various parameter values. Table S3. Genomes excluded i

DataCite Commons2020-08-26 更新50

The Cost-Effectiveness of Reclassification Sampling for Prevalence Estimation

流行病学抽样方法

分类器性能评估

BackgroundTypically, a two-phase (double) sampling strategy is employed when classifications are subject to error and there is a gold standard (perfect) classifier available. Two-phase sampling involv

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Leave-one-out cross-validation results.

分类器性能评估

自学习分类器

Recall, precision, and f-measure are calculated for each class. Weighted f-measure, fW, and average accuracy, Acc, are calculated for all classes of a problem. All assessments are based on leave-one-o

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Experimental results of different SE-ResNet architectures on Cifar.

资源简介：

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