图 书 馆
内核机器的距离度量学习Distance Metric Learning for Kernel Machines |
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| 课程网址: | http://videolectures.net/nipsworkshops2010_weinberger_dml/ |
| 主讲教师: | Kilian Weinberger |
| 开课单位: | 康奈尔大学 |
| 开课时间: | 2011-01-12 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Recent work in metric learning has significantly improved the state-of-the-art in k-nearest neighbor classification. However, Support vector machines (with RBF kernels) are arguably the most popular class of classification algorithms that uses distance metrics to compare examples. In this talk I will introduce support vector metric learning (SVML), an algorithm that seamlessly combines both by learning a Mahalanobis metric at the same time as the RBF-SVM decision boundary. SVML is an effective tool for automatically pre-processing data sets for classification, as well as visualizing the structure of SVM decision boundaries. We demonstrate the capabilities (and shortcomings) of our algorithm on 10 benchmark data sets of varying sizes and difficulties. |
| 关 键 词: | 度量学习; 支持向量机; 基准数据集 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2020-06-02：毛岱琦(课程编辑志愿者) |
| 阅读次数: | 75 |