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平滑接收机工作特性曲线(SMROC)

Smooth Receiver Operating Characteristics Curves (smROC)
课程网址: http://videolectures.net/solomon_klement_smroc/  
主讲教师: William Klement
开课单位: 渥太华大学
开课时间: 2011-10-10
课程语种: 英语
中文简介:
监督学习算法执行常见的任务,包括分类,排名,评分和概率估计。我们研究了通常由这些模型产生的评分信息如何通过评估手段来利用。 ROC曲线表示分类器排名表现的可视化。但是,他们忽略了可能很有参考价值的得分。尽管这种被忽略的信息不如概率给出的信息那么精确,但它比排名所传达的信息更为详尽。本文提出了一种通过这些分数加权ROC曲线的新方法。我们将其称为“平滑ROC(smROC)”曲线,并演示如何将其用于可视化学习模型的性能。我们报告的实验结果表明,smROC适用于测量学习模型之间的性能相似性和差异,并且比标准ROC曲线对性能特征更敏感。
课程简介: Supervised learning algorithms perform common tasks including classification, ranking, scoring, and probability estimation. We investigate how scoring information, often produced by these models, is utilized by an evaluation measure. The ROC curve represents a visualization of the ranking performance of classifiers. However, they ignore the scores which can be quite informative. While this ignored information is less precise than that given by probabilities, it is much more detailed than that conveyed by ranking. This paper presents a novel method to weight the ROC curve by these scores. We call it the Smooth ROC (smROC) curve, and we demonstrate how it can be used to visualize the performance of learning models. We report experimental results to show that the smROC is appropriate for measuring performance similarities and differences between learning models, and is more sensitive to performance characteristics than the standard ROC curve.
关 键 词: 监督学习; 分类器排名; 可视化学习
课程来源: 视频讲座网
最后编审: 2019-09-23:cwx
阅读次数: 39