首页/数据集/Performance of different SVM classifiers in LOO CV.

Performance of different SVM classifiers in LOO CV.

NIAID Data Ecosystem2026-03-06 收录

分类器性能评估

支持向量机模型比较

数据链接：

https://figshare.com/articles/dataset/_Performance_of_different_SVM_classifiers_in_LOO_CV_/529279 数据链接链接失效反馈

官方服务：

资源简介：

Th–Threshold, SN–sensitivity, SP–specificity, MCC–Matthews Correlation Coefficient.

应用场景：

创建时间：

2010-03-15

图像色彩特征分析

分类器性能评估

A) Average error of all classifiers and descriptors, B) ROC curves for Classification using AdaBoost and Bagging with intensity and CMYK&Hb&Lb&HSV&Lab colour model combination.

NIAID Data Ecosystem70

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

NIAID Data Ecosystem60

Statistical significance of linear Vs non-linear classification.

意识状态评估

分类器性能评估

Statistical significance of differences in performance of the different classifiers at loss and recovery of consciousness (LOC and ROC respectively). Classifiers: linear (SVML) and nonlinear (SVMNL) S

Figshare2015-12-02 更新30

Additional file 2: of The parameter sensitivity of random forests

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

NIAID Data Ecosystem60

A battery of metrics of five classifiers in the training and validation cohorts for fivefold nested cross-validation.

分类器性能评估

交叉验证

A battery of metrics of five classifiers in the training and validation cohorts for fivefold nested cross-validation.

NIAID Data Ecosystem40