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Description: A Perl module implementing receiver-operator-characteristic (ROC)
curves with nonparametric confidence bounds
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Size: 17519 |
Author: 刘国亮 |
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Description: In this demo, I use the EM algorithm with a Rauch-Tung-Striebel smoother and an M step, which I ve recently derived, to train a two-layer perceptron, so as to classify medical data (kindly provided by Steve Roberts and Will Penny from EE, Imperial College). The data and simulations are described in: Nando de Freitas, Mahesan Niranjan and Andrew Gee Nonlinear State Space Estimation with Neural Networks and the EM algorithm After downloading the file, type \"tar -xf EMdemo.tar\" to uncompress it. This creates the directory EMdemo containing the required m files. Go to this directory, load matlab5 and type \"EMtremor\". The figures will then show you the simulation results, including ROC curves, likelihood plots, decision boundaries with error bars, etc. WARNING: Do make sure that you monitor the log-likelihood and check that it is increasing. Due to numerical errors, it might show glitches for some data sets.
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Size: 198220 |
Author: 晨间 |
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Description: 计算ROC曲线和AUC的Matlab程序-Calculation of ROC curves and AUC of Matlab procedures
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Size: 1024 |
Author: 刘国亮 |
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Description: A Perl module implementing receiver-operator-characteristic (ROC)
curves with nonparametric confidence bounds
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Size: 17408 |
Author: 刘国亮 |
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Description: In this demo, I use the EM algorithm with a Rauch-Tung-Striebel smoother and an M step, which I ve recently derived, to train a two-layer perceptron, so as to classify medical data (kindly provided by Steve Roberts and Will Penny from EE, Imperial College). The data and simulations are described in: Nando de Freitas, Mahesan Niranjan and Andrew Gee Nonlinear State Space Estimation with Neural Networks and the EM algorithm After downloading the file, type "tar -xf EMdemo.tar" to uncompress it. This creates the directory EMdemo containing the required m files. Go to this directory, load matlab5 and type "EMtremor". The figures will then show you the simulation results, including ROC curves, likelihood plots, decision boundaries with error bars, etc. WARNING: Do make sure that you monitor the log-likelihood and check that it is increasing. Due to numerical errors, it might show glitches for some data sets.
-In this demo, I use the EM algorithm with a Rauch-Tung-Striebel smoother and an M step, which I ve recently derived, to train a two-layer perceptron, so as to classify medical data (kindly provided by Steve Roberts and Will Penny from EE, Imperial College). The data and simulations are described in: Nando de Freitas, Mahesan Niranjan and Andrew Gee Nonlinear State Space Estimation with Neural Networks and the EM algorithm After downloading the file, type "tar-xf EMdemo.tar" to uncompress it. This creates the directory EMdemo containing the required m files. Go to this directory, load matlab5 and type "EMtremor". The figures will then show you the simulation results, including ROC curves, likelihood plots, decision boundaries with error bars, etc. WARNING: Do make sure that you monitor the log-likelihood and check that it is increasing. Due to numerical errors, it might show glitches for some data sets.
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Size: 197632 |
Author: 晨间 |
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Description:
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Size: 1024 |
Author: zc |
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Description:
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Size: 6144 |
Author: xiaoli |
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Description: ROC curves illustrate performance on a binary classification problem where classification is based on simply thresholding a set of scores at varying levels. Lenient thresholds give high sensitivity but low specificity, strict thresholds give high specificity but low sensitivity the ROC curve plots this trade-off over a range of thresholds (usually with sens vs 1-spec, but I prefer sens vs spec this code gives you the option).
It is theoretically possible to operate anywhere on the convex hull of an ROC curve, so this is plotted too. The area under the curve (AUC) for a ROC plot is a measure of overall accuracy, and the area under the ROCCH is a kind of upper bound on what might be achievable with a weighted combination of differently thresholded results from the given classifier
-ROC curves illustrate performance on a binary classification problem where classification is based on simply thresholding a set of scores at varying levels. Lenient thresholds give high sensitivity but low specificity, strict thresholds give high specificity but low sensitivity the ROC curve plots this trade-off over a range of thresholds (usually with sens vs 1-spec, but I prefer sens vs spec this code gives you the option).
It is theoretically possible to operate anywhere on the convex hull of an ROC curve, so this is plotted too. The area under the curve (AUC) for a ROC plot is a measure of overall accuracy, and the area under the ROCCH is a kind of upper bound on what might be achievable with a weighted combination of differently thresholded results from the given classifier
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Size: 4096 |
Author: saadat |
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Description: 画出函数的ROC曲线，precision/recall 曲线-function [prec, tpr, fpr, thresh] = prec_rec(score, target, varargin)
PREC_REC- Compute and plot precision/recall and ROC curves.
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Size: 4096 |
Author: zhangrui |
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Description: 关于用SPSS来求取ROC曲线的文档，文中主要讲述了求ROC曲线的具体过程-About with SPSS to account the ROC curves of documents, this paper mainly tells the ROC curves for the detailed process
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Size: 101376 |
Author: mike |
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Description: ROC曲线，可以实现统计上对ROC的计算-ROC curves, statistical calculation of ROC
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Size: 5120 |
Author: hunter |
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Description: EM算法在神经网络中的应用，可以用来进行视频数据分类。-In this demo, I use the EM algorithm with a Rauch-Tung-Striebel smoother and an M step, which I ve recently derived, to train a two-layer perceptron, so as to classify medical data (kindly provided by Steve Roberts and Will Penny from EE, Imperial College). The data and simulations are described in: Nando de Freitas, Mahesan Niranjan and Andrew Gee Nonlinear State Space Estimation with Neural Networks and the EM algorithm After downloading the file, type "tar-xf EMdemo.tar" to uncompress it. This creates the directory EMdemo containing the required m files. Go to this directory, load matlab5 and type "EMtremor". The figures will then show you the simulation results, including ROC curves, likelihood plots, decision boundaries with error bars, etc. WARNING: Do make sure that you monitor the log-likelihood and check that it is increasing. Due to numerical errors, it might show glitches for some data sets.
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Size: 14336 |
Author: gaofei |
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Description: This a methodology of robust and secure watermarking scheme as described in the following paper http://www.ias.ac.in/sadhana/Pdf2012Aug/425.pdf. Besides that the code implements ROC curves and different methods of attacks to justify the robustness of the scheme.-This is a methodology of robust and secure watermarking scheme as described in the following paper http://www.ias.ac.in/sadhana/Pdf2012Aug/425.pdf. Besides that the code implements ROC curves and different methods of attacks to justify the robustness of the scheme.
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Size: 1950720 |
Author: Akshay |
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Description: 利用matlab m文件，实现了接收机ROC性能曲线的绘制-Use matlab m files, to achieve the performance of ROC curves plotted receiver
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Size: 28672 |
Author: b |
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Description: The receiver performance is quantified by depicting the receiver operating char- acteristics (ROC) curves. This curves serve as a tool to and study the performance of a sensing scheme. ROC graphs are employed to show trade-of vs between detection probability and false alarm rates, (i.e. PD versus PFA), thus allowing the determination of an optimal threshold. To illustrate the performance of energy detectors, we generate complementary ROC curves (the average probability of miss detection $ P_{md} 1 - P_d$ versus the false alarm probability $P_f$ for different receiver structures and in different fading environments.Fig. 1 illustrates the complementary ROC curves for a single channel reception (no diversity) over AWGN channels. This figure depicts the complementary ROC curves for energy detectors.
-The receiver performance is quantified by depicting the receiver operating char- acteristics (ROC) curves. This curves serve as a tool to and study the performance of a sensing scheme. ROC graphs are employed to show trade-of vs between detection probability and false alarm rates, (i.e. PD versus PFA), thus allowing the determination of an optimal threshold. To illustrate the performance of energy detectors, we generate complementary ROC curves (the average probability of miss detection $ P_{md} 1 - P_d$ versus the false alarm probability $P_f$ for different receiver structures and in different fading environments.Fig. 1 illustrates the complementary ROC curves for a single channel reception (no diversity) over AWGN channels. This figure depicts the complementary ROC curves for energy detectors.
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Size: 1024 |
Author: sagar |
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Description: Everything ROC and Precision-Recall curves.
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Size: 66560 |
Author: mhk |
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Description: 多重相关系数的ROC曲线，说明此算法的检测概率与虚警概率的性能比其他经典频谱感知算法更好(The ROC curves of multiple correlation coefficients show that the performance of detection probability and false alarm probability of this algorithm is better than that of other classical spectrum sensing algorithms)
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Size: 1024 |
Author: ninihan
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Description: 对于Swerling 1起伏目标模型的ROC曲线的仿真(Simulation of ROC curves for Swerling 1 fluctuating target model)
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Size: 1024 |
Author: 突突突
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Description: 计算信号检测时的ROC曲线,计算结果比较准确，完全符合要求。(Computed the ROC curves.)
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Size: 3072 |
Author: chengzitnt
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Description: 三个程序实现目标检测结果的评估，包括ROC曲线，AP值,Fmeasure的计算。(Three procedures are used to evaluate the results of target detection, including ROC curves, AP values, and Fmeasure calculations.)
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Size: 3072 |
Author: sherry3255
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