图 书 馆
二元分类量正则化Volume Regularization for Binary Classification |
|
| 课程网址: | http://videolectures.net/machine_crammer_volume_regularization/ |
| 主讲教师: | Koby Crammer |
| 开课单位: | 以色列理工学院 |
| 开课时间: | 2013-06-14 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | We introduce a large-volume box classification for binary prediction, which maintains a subset of weight vectors, and specifically axis-aligned boxes. Our learning algorithm seeks for a box of large volume that contains "simple" weight vectors which most of are accurate on the training set. Two versions of the learning process are cast as convex optimization problems, and it is shown how to solve them efficiently. The formulation yields a natural PAC-Bayesian performance bound and it is shown to minimize a quantity directly aligned with it. The algorithm outperforms SVM and the recently proposed AROW algorithm on a majority of 30 NLP datasets and binarized USPS optical character recognition datasets. |
| 关 键 词: | 大体积箱分类; 二进制预测; 算法; 贝叶斯性能 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2020-06-28：yumf |
| 阅读次数: | 49 |