Search - Matlab AUC

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[Other resource] ROCandAUC DL : 0: 计算ROC曲线和AUC的Matlab程序
Update : 2008-10-13 Size : 1.07kb Publisher : 刘国亮
[Algorithm] ROCandAUC DL : 0: 计算ROC曲线和AUC的Matlab程序-Calculation of ROC curves and AUC of Matlab procedures
Update : 2025-02-19 Size : 1kb Publisher : 刘国亮
[Communication] rocplot DL : 0: 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
Update : 2025-02-19 Size : 4kb Publisher : saadat
[AI-NN-PR] Bayes_EM DL : 1: 利用matlab实现的基于EM算法的贝叶斯分类器的源代码，可以用来分类或识别，很值得收藏-Using matlab to achieve EM algorithm based on Bayesian classifier of the source code can be used to classification or identification, it is worthy of collection
Update : 2025-02-19 Size : 11kb Publisher : satanwings
[matlab] ROC-and-AUC-analysis DL : 0: A good matlab code that analysis the ROC curve and corresponding AUC value to estimate the sensitive and the currectness of the sample estimate. This code is suitable for variable type and function data.
Update : 2025-02-19 Size : 3kb Publisher : lihuijia
[AI-NN-PR] OPAUC DL : 0: One-Pass AUC 优化的matlab代码。参考文献：Wei Gao, Rong Jin, Shenghou Zhu and Zhi-Hua Zhou. One-Pass AUC Optimzation. In: Proceedings of the 30th International conference on Machine Learning (ICML 13), Atlanta, GA, 2013, JMLR: W&CP 28(3), pp.906-914. -This package includes the MATLAB code of One-Pass AUC Optimization
Update : 2025-02-19 Size : 2kb Publisher : shaokai
[Video Capture] DLTcode DL : 0: Robust Non-negative Dictionary Learning for Visual Tracking The provided codes could be either embedded into the benchmark framework of paper Online Object Tracking: A Benchmark (CVPR2013) (You can find details here: http://visual-tracking.net/) or run on individual sequence. To run the benchmark, just put the entire folder into the /trackers folder in the benchmark code base, and modify the configTrackers.m in util folder. DLT gets an AUC of 0.436, which ranks 5th among 26 in the benchmark by 19/03/2014. We don t tune parameters for single sequence in this case, all the parameters are stored in trackparam_DLT.m. To run on individual video, you need to modify the dataPath and title in run_individual.m. If you run MATLAB version after 2012, and have a CUDA compatible GPU installed, you may enjoy the fast computation speed by GPU, just set useGPU to true in trackparam_DLT.m and run_individual.m! -Robust Non-negative Dictionary Learning for Visual Tracking The provided codes could be either embedded into the benchmark framework of paper Online Object Tracking: A Benchmark (CVPR2013) (You can find details here: http://visual-tracking.net/) or run on individual sequence. To run the benchmark, just put the entire folder into the /trackers folder in the benchmark code base, and modify the configTrackers.m in util folder. DLT gets an AUC of 0.436, which ranks 5th among 26 in the benchmark by 19/03/2014. We don t tune parameters for single sequence in this case, all the parameters are stored in trackparam_DLT.m. To run on individual video, you need to modify the dataPath and title in run_individual.m. If you run MATLAB version after 2012, and have a CUDA compatible GPU installed, you may enjoy the fast computation speed by GPU, just set useGPU to true in trackparam_DLT.m and run_individual.m!
Update : 2025-02-19 Size : 21.18mb Publisher : mohit
[matlab] ROC DL : 0: 画ROC曲线,并求取相应的pf,pd值，求AUC值的matlab代码(Draw the ROC curve, and solve the PD, PF values)
Update : 2025-02-19 Size : 706kb Publisher : jojo7725
[Other] matlab DL : 0: 对于一个具体的数据，用交叉验证进行分类，随机森林进行训练，用AUC,AUPR,Precision评价分类器的性能(For a specific data, use cross validation to classify, train random forests, evaluate the performance of the classifier with AUC, AUPR, and Precision.)
Update : 2025-02-19 Size : 19kb Publisher : Katherine_