图 书 馆
多学科fMRI数据的分层高斯朴素贝叶斯分类器Hierarchical Gaussian Naive Bayes Classifier for Multiple-Subject fMRI Data |
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| 课程网址: | http://videolectures.net/fmri06_rustandi_hgnbc/ |
| 主讲教师: | Indrayana Rustandi |
| 开课单位: | 普林斯顿大学 |
| 开课时间: | 2007-02-25 |
| 课程语种: | 英语 |
| 中文简介: |  高斯Na＆uml;ıveBayes（GNB）[2]分类器已成功应用于fMRI数据。然而，它并非专门用于计算来自多个受试者的数据，并且通常应用于来自单个受试者的数据（称为GNB indiv）。已经提出了对GNB分类器的扩展（[4]，称为GNB合并），其中来自所有主体的数据通过假设它们都来自相同的主题而组合在一起。但是，此扩展名忽略了可能存在的主题特定变体。在这里，我描述了GNB分类器的另一个扩展＆mdash;分层GNB分类器[3]＆mdash;它可以解释主题特定的变化，此外，还可以灵活地增加或减少来自其他主题的数据贡献的权重根据测试对象提供的示例数量。 |
| 课程简介: | The Gaussian Na¨ıve Bayes (GNB) [2] classifier has been successfully applied to fMRI data. However, it is not specifically designed to account for data from multiple subjects and is usually applied to data from a single subject (referred to as GNB-indiv). An extension to the GNB classifier has been proposed ([4], referred to as GNB-pooled), in which the data from all the subjects are combined together na¨ıvely by assuming that they all come from the same subjects. However, this extension ignores subject-specific variations that might exist. Here I describe another extension of the GNB classifier—the hierarchical GNB classifier [3]—that can account for subject-specific variations, and in addition, has the flexibility to increase or reduce the weight of the contribution of the data from the other subjects based on the number of examples available from the test subject. |
| 关 键 词: | 高斯分类器; 受试者; 拓展名 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-04-14：cwx |
| 阅读次数: | 78 |