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特征空间中的指数族- 第6部分

Exponential Families in Feature Space - Part 6
课程网址: http://videolectures.net/mlss05au_vishwanathan_effs6/  
主讲教师: Vishwanathan S.V.N
开课单位: 加利福尼亚大学
开课时间: 2007-02-25
课程语种: 英语
中文简介:
在这门入门课程中,我们将讨论如何将对数线性模型扩展到特征空间。这些对数线性模型长期以来一直被统计学家以概率分布的指数族的名义研究。我们提供了一个统一的框架,可以将现有的许多内核算法视为特殊情况。我们的框架还允许我们推导现有算法的许多自然概括。特别地,我们展示了如何恢复高斯过程、支持向量机、多类识别和序列注释(通过条件随机字段)。我们还展示了如何处理丢失的数据,并对特征空间中的条件随机字段进行映射估计。这门课程的必要背景将在前两堂课中很快地介绍。了解线性代数和熟悉泛函分析将有帮助。
课程简介: In this introductory course we will discuss how log linear models can be extended to feature space. These log linear models have been studied by statisticians for a long time under the name of exponential family of probability distributions. We provide a unified framework which can be used to view many existing kernel algorithms as special cases. Our framework also allows us to derive many natural generalizations of existing algorithms. In particular, we show how we can recover Gaussian Processes, Support Vector Machines, multi-class discrimination, and sequence annotation (via Conditional Random Fields). We also show to deal with missing data and perform MAP estimation on Conditional Random Fields in feature space. The requisite background for the course will be covered briskly in the first two lectures. Knowledge of linear algebra and familiarity with functional analysis will be helpful.
关 键 词: 高斯过程; 支持向量机; 多类歧视; 序列注释; 线性代数
课程来源: 视频讲座网
最后编审: 2020-06-06:张荧(课程编辑志愿者)
阅读次数: 55