图 书 馆
大型线性分类的最新进展Recent Advances in Large Linear Classification |
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| 课程网址: | http://videolectures.net/acml2013_lin_large_linear_classification... |
| 主讲教师: | Chih-Jen Lin |
| 开课单位: | 斯坦福大学 |
| 开课时间: | 2014-03-27 |
| 课程语种: | 英语 |
| 中文简介: |  线性分类是机器学习和数据挖掘中的有用工具。对于丰富维空间中的某些数据，线性分类器的预测性能已显示出接近非线性分类器（例如核方法）的预测性能，但是训练和测试速度要快得多。最近，许多研究工作提出了用于构造线性分类器的有效优化方法。我们简要讨论其中一些在我们开发软件LIBLINEAR时考虑的问题。然后，我们开始讨论线性分类的一些扩展。特别地，线性分类器对于直接或间接地近似内核分类器可能是有用的。我将展示一些真实的单词示例，在保持竞争准确性的同时，我们将尝试这些示例以实现快速的培训/测试速度。最后，将讨论该研究主题的未来挑战，特别是大数据线性分类方面的挑战。 |
| 课程简介: | Linear classification is a useful tool in machine learning and data mining. For some data in a rich dimensional space, the prediction performance of linear classifiers has shown to be close to that of nonlinear classifiers such as kernel methods, but training and testing speed is much faster. Recently, many research works have proposed efficient optimization methods to construct linear classifiers. We briefly discuss some of them that were considered in our development of the software LIBLINEAR. We then move to discuss some extensions of linear classification. In particular, linear classifiers can be useful to either directly or indirectly approximate kernel classifiers. I will show some real-word examples for which we try to achieve fast training/testing speed, while maintain competitive accuracy. Finally, future challenges of this research topic, in particular, aspects on big-data linear classification, will be discussed. |
| 关 键 词: | 数据挖掘; 机器学习 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2020-12-07：zyk |
| 最后编审: | 2020-12-07：zyk |
| 阅读次数: | 29 |