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多类别学习方法：理论上与启示的比较Multiclass Learning Approaches: A Theoretical Comparison with Implications |
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| 课程网址: | http://videolectures.net/machine_daniely_learning/ |
| 主讲教师: | Amit Daniely |
| 开课单位: | 耶路撒冷希伯来大学 |
| 开课时间: | 2013-06-14 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | We theoretically analyze and compare the following five popular multiclass classification methods: One vs. All, All Pairs, Tree-based classifiers, Error Correcting Output Codes (ECOC) with randomly generated code matrices, and Multiclass SVM. In the first four methods, the classification is based on a reduction to binary classification. We consider the case where the binary classifier comes from a class of VC dimension d, and in particular from the class of halfspaces over \reals d. We analyze both the estimation error and the approximation error of these methods. Our analysis reveals interesting conclusions of practical relevance, regarding the success of the different approaches under various conditions. Our proof technique employs tools from VC theory to analyze the \emph{approximation error} of hypothesis classes. This is in sharp contrast to most, if not all, previous uses of VC theory, which only deal with estimation error. |
| 关 键 词: | 分类方法; 代码矩阵; 近似误差 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-05-15：cwx |
| 阅读次数: | 58 |