图的价值回归Graph-Valued Regression |
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课程网址: | http://videolectures.net/nips2010_chen_gvr/ |
主讲教师: | Xi Chen |
开课单位: | 卡内基梅隆大学 |
开课时间: | 2011-03-25 |
课程语种: | 英语 |
中文简介: | 无向图形模型在图G中编码随机向量y的依赖结构。在许多应用中,考虑到另一个随机向量x作为输入,对y进行建模是很有意义的。我们将x=x条件下y的图g(x)估计为图值回归问题。本文提出了一种半参数估计G(X)的方法,它在X空间上建立一棵树,就像在Cart(分类树和回归树)中那样,但是在树的每一片叶子上估计一个图。我们称之为“图表优化购物车”或“购物车”。利用二元划分树研究了Go Cart的理论性质,建立了风险最小化和树划分一致性的Oracle不等式。我们还演示了GoCart在气象数据集中的应用,展示了图值回归如何为分析复杂数据提供有用的工具。 |
课程简介: | Undirected graphical models encode in a graph G the dependency structure of a random vector Y. In many applications, it is of interest to model Y given another random vector X as input. We refer to the problem of estimating the graph G(x) of Y conditioned on X=x as "graph-valued regression". In this paper, we propose a semiparametric method for estimating G(x) that builds a tree on the X space just as in CART (classification and regression trees), but at each leaf of the tree estimates a graph. We call the method "Graph-optimized CART", or Go-CART. We study the theoretical properties of Go-CART using dyadic partitioning trees, establishing oracle inequalities on risk minimization and tree partition consistency. We also demonstrate the application of Go-CART to a meteorological dataset, showing how graph-valued regression can provide a useful tool for analyzing complex data. |
关 键 词: | 随机向量; 图值回归; 风险最小化 |
课程来源: | 视频讲座网 |
最后编审: | 2020-06-02:毛岱琦(课程编辑志愿者) |
阅读次数: | 43 |