Search - receiver operating characteristic curve

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[matlab] Elements_courbe_ROC DL : 0: Cours Roc Curve tres utiles Rappels sur la courbe ROC (Receiver Operating Characteristic Curve) Supposons qu une population soit répartie en deux classes notées respectivement 1 et 0. Mais, au lieu d observer directement la classe à laquelle appartient un sujet, on dispose de renseignements sur chaque sujet, permettant d en déduire la classe à laquelle il appartient avec une certaine probabilité d erreur.
Update : 2025-03-07 Size : 24kb Publisher : salwa
[AI-NN-PR] Rocforsvm DL : 0: 通过决策值可以绘制出ROC曲线的python程序-This tool which gives the ROC (Receiver Operating Characteristic) curve and AUC (Area Under Curve) by ranking the decision values.
Update : 2025-03-07 Size : 6kb Publisher : quarryhero
[matlab] ROCcurve DL : 0: Update : 2025-03-07 Size : 4kb Publisher : zinyyy
[matlab] ROC-plot DL : 0: 试者工作特征曲线（receiver operating characteristic curve，简称ROC曲线），又称为感受性曲线（sensitivity curve）。得此名的原因在于曲线上各点反映着相同的感受性，它们都是对同一信号刺激的反应，只不过是在几种不同的判定标准下所得的结果而已。接受者操作特性曲线就是以虚报概率为横轴，击中概率为纵轴所组成的坐标图，和被试在特定刺激条件下由于采用不同的判断标准得出的不同结果画出的曲线。-PLOTROC Plot receiver operating characteristic. Syntax plotroc(targets,outputs) plotroc(targets1,outputs1, name1 ,targets,outputs2, name2 , ...) Description PLOTROC(TARGETS,OUTPUTS) plots the receiver operating characteristic for each output class. The more each curve hugs the left and top edges of the plot, the better the classification. PLOTROC(TARGETS1,OUTPUTS2, name1 ,...) generates multiple plots.
Update : 2025-03-07 Size : 5kb Publisher : 李明
[Software Engineering] rev5 DL : 1: 基于蒙特卡洛方法的主动声纳信号检测性能分析.主动声纳信号检测性能的分析上,目前在计算机仿真中一般假定混响包络的统计特性符合瑞利分布模型。基于此模型,已经有了较完善的理论。然而,在现代高分辨声纳系统中,混响包络的统计特性并不符合瑞利分布模型。此时在接收机工作特性分析时存在大量繁琐的公式推导。因此该文采用蒙特卡洛(M onte Carlo)统计试验方法,实现对瑞利分布混响背景下的主动声纳信号检测性能分析。结合对接收机工作特性曲线的仿真,得出了检测概率的理论值和仿真结果的误差曲线。误差曲线表明,蒙特卡洛方法在主动声纳信号检测的性能评估中是可行的。更多还原 -Active sonar signal detection performance analysis, computer simulation is generally assumed that the statistical properties of the reverberation envelope distribution model of Rayleigh active sonar signal detection performance analysis based on the Monte Carlo method. Based on this model, has been a better theory. However, in the modern high-resolution sonar system, the statistical characteristics of the reverberation envelope does not comply with the Rayleigh distribution model. At this point in the receiver operating characteristic analysis, there is a lot of tedious formula derivation. In this paper, using the Monte Carlo (M onte Carlo) statistical test methods, active sonar signal detection performance analysis reverberation background Rayleigh distribution. The receiver operating characteristic curve simulation obtained the error curve of the theoretical value and the simulation results of the detection probability. The error curve shows that the Monte Carlo method in the assessm
Update : 2025-03-07 Size : 171kb Publisher : cooldog
[3G develop] Program_Energy-Detection DL : 0: This code is to plot receiver operating characteristic curve for simple energy detection, when the primary signal is real Gaussian signal and noise is addive white real Gaussian. Here, the threshold is available analytically. India. - This code is to plot receiver operating characteristic curve for simple energy detection, when the primary signal is real Gaussian signal and noise is addive white real Gaussian. Here, the threshold is available analytically. India.
Update : 2025-03-07 Size : 11kb Publisher : Joydev Ghosh
[Program doc] Energy_detection_simulation DL : 0: This code is to plot receiver operating characteristic curve for simple energy detection, when the primary signal is real Gaussian signal and noise is addive white real Gaussian. Here, the threshold is available analytically.
Update : 2025-03-07 Size : 2kb Publisher : burak
[Other] AUC计算器 DL : 0: 计算AUC的小程序 ROC的全名叫做Receiver Operating Characteristic，其主要分析工具是一个画在二维平面上的曲线——ROC curve。平面的横坐标是false positive rate(FPR)，纵坐标是true positive rate(TPR)。对某个分类器而言，我们可以根据其在测试样本上的表现得到一个TPR和FPR点对。这样，此分类器就可以映射成ROC平面上的一个点。调整这个分类器分类时候使用的阈值，我们就可以得到一个经过(0, 0)，(1, 1)的曲线，这就是此分类器的ROC曲线。一般情况下，这个曲线都应该处于(0, 0)和(1, 1)连线的上方。因为(0, 0)和(1, 1)连线形成的ROC曲线实际上代表的是一个随机分类器。如果很不幸，你得到一个位于此直线下方的分类器的话，一个直观的补救办法就是把所有的预测结果反向，即：分类器输出结果为正类，则最终分类的结果为负类，反之，则为正类。虽然，用ROC curve来表示分类器的performance很直观好用。可是，人们总是希望能有一个数值来标志分类器的好坏。于是Area Under roc Curve(AUC)就出现了。顾名思义，AUC的值就是处于ROC curve下方的那部分面积的大小。通常，AUC的值介于0.5到1.0之间，较大的AUC代表了较好的performance。好了，到此为止，所有的前续介绍部分结束，下面进入本篇帖子的主题：AUC的计算方法总结。
Update : 2014-08-21 Size : 10.21kb Publisher : xiaobena
[matlab] EER DL : 0: Plot This curves Receiver Operating Characteristic (ROC) curve Detection Error Trade-off (DET) curve FAR vs FRR
Update : 2025-03-07 Size : 1kb Publisher : yasser
[Other] energy_detection_simulation_exact_Rayleigh DL : 0: This code is to plot receiver operating characteristic curve for simple energy detection, when the primary signal is real Gaussian signal and noise is additive white real Gaussian. Here, the threshold is available analytically.
Update : 2025-03-07 Size : 3kb Publisher : mahru
[Software Engineering] receiver DL : 0: This code is to plot receiver operating characteristic curve for simple energy detection
Update : 2025-03-07 Size : 1kb Publisher : Nick