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Performance measures of the SVM classifier based on input features.

Figshare2019-04-03 更新2026-04-29 收录

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https://figshare.com/articles/dataset/Performance_measures_of_the_SVM_classifier_based_on_input_features_/7947272 数据链接链接失效反馈

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Performance measures of the SVM classifier based on input features.

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创建时间：

2019-04-03

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