图 书 馆
通过模式分析和核方法的方法自适应建模Adaptive Modelling via Pattern Analysis and the Kernel Methods approach |
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| 课程网址: | http://videolectures.net/sscs06_taylor_amvpa/ |
| 主讲教师: | John Shawe-Taylor |
| 开课单位: | 伦敦大学学院 |
| 开课时间: | 2007-02-25 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | There is a dramatic growth in the availability of complex data from a wide range of different applications. The challenge of the data analyzer is to extract knowledge from the raw data by identifying the useful patterns and structures that underlie it. This module introduces adaptive and probabilistic approaches to modeling such complex data. We first consider finding structure in high-dimensional data. The kernel methods approach to identifying non-linear patterns in introduced while addressing the issues of statistical reliability of inferences made from limited data. Subspace identification is considered and correlations across different data modalities are shown to provide a useful approach to eliciting semantic representations. The final section of the course will introduce learning probabilistic models, (e.g. in biological sequence data), fusing prior knowledge and data, complex and approximate inference. |
| 关 键 词: | 计算机科学; 机器学习; 核方法 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2020-10-22：chenxin |
| 阅读次数: | 36 |