图 书 馆
学习的内在几何学The Intrinsic Geometries of Learning |
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| 课程网址: | http://videolectures.net/etvc08_nock_tigol/ |
| 主讲教师: | Richard Nock |
| 开课单位: | 子宫内膜异位症研究中心 |
| 开课时间: | 2008-12-05 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | In a seminal paper, Amari (1998) proved that learning can be made more efcient when one uses the intrinsic Riemannian structure of the algorithms' spaces of parameters to point the gradient towards better solutions. In this paper, we show that many learning algorithms, including various boosting algorithms for linear separators, the most popular top-down decision-tree induction algorithms, and some on-line learning algorithms, are spawns of a generalization of Amari's natural gradient to some particular non-Riemannian spaces. These algorithms exploit an intrinsic dual geometric structure of the space of parameters in relationship with particular integral losses that are to be minimized. We unite some of them, such as AdaBoost, additive regression with the square loss, the logistic loss, the top-down induction performed in CART and C4.5, as a single algorithm on which we show general convergence to the optimum and explicit convergence rates under very weak assumptions. As a consequence, many of the classi cation calibrated surrogates of Bartlett et al. (2006) admit efficient minimization algorithms. |
| 关 键 词: | 黎曼结构; 学习算法; 决策树归纳算法 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-10-17：cwx |
| 阅读次数: | 63 |