The ensemble model of logistic regression and support vector classifier showed the highest area under the curve value among all the other models [area under the curve=0.808, 95% confidence interval (CI): 0.708-0.894]. The sensitivity and specificity of the ensemble model were 73.7% (95% CI:...
Notes: AUC, area under the curve; RF, random forest; LR, logistic regression; SVM, support vector machine; fMRI, functional magnetic resonance imaging; sMRI, structural magnetic resonance imaging; combined fMRI indices included ALFF, fALFF, ReHo and DC; combined sMRI indices included GWV, WMV...
虽然文中提出的CurveNet与meta-weight-net参数更新方法几乎一致,但meta-weight-net论文中,meta-weight-net的输入(也就是loss)随着训练而变化,无法代表样本的整体训练状态。 此外,meta-weight-net只能处理单一的数据偏置,两类偏置一起的情况原文中并未对其测试。而改进的CurveNet可以同时处理两类偏置。 Method Meta-we...
The resulting prediction curve for the same data points as those used for Example 11.2. The improved performance compared to the kernel ridge regression used for Fig. 11.9 is readily observed. The encircled points are the support vectors resulting from the optimization, using the ϵ-insensitive ...
The black curve is the nullcline given by the known part of the differential equation, the dashed red (blue) curve is the second nullcline corresponding to the bistable resp. oscillatory state (to the monostable resp. steady state) in (a) resp. (b). This figure is plotted using Julia ...
pytorchface-recognitionroc-curvelfwcenter-loss UpdatedNov 11, 2020 Python LeslieZhoa/tensorflow-facenet Star153 人脸识别算法,结合facenet网络结构和center loss作为损失,基于tensorflow框架,含训练和测试代码,支持从头训练和摄像头测试 tensorflowchineseface-recognitionfacenetcenter-loss ...
4.1. This curve highlights a typical asymmetric profile where small losses have higher chance than greater losses. At the same time, Fig. 4.1 points out the distinction between expected loss and unexpected loss. The latter is obtained as a difference between a given quantile of the distribution ...
(MCC). Therefore, for each benchmark receiver operating characteristic area under curve (ROC-AUC), precision-recall area under curve (PR-AUC), accuracy, balanced accuracy, recall, precision, F1 score and MCC were measured. A more in-depth discussion on the choice of metrics and their ...
On the other hand, the abstract curve-fitting nature of ANNs renders them dangerously susceptible to extrapolation errors. Consider SI, for example. In the forecast of a loss reserve, one needs to make some assumption for the future. A GLM will have estimated past SI, and while this might ...
{0.005, 0.0005, 0.00005}. A weight-decay value of 0.0005 resulted in the best performance in terms of area under the curve for accuracy on the test set over the 20 increments. For Shrink and Perturb, we used the weight-decay value of the base system and tested values for the ...