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稀疏贝叶斯非参数回归

Sparse Bayesian Nonparametric Regression
课程网址: http://videolectures.net/icml08_caron_sbnr/  
主讲教师: François Caron
开课单位: INRIA Bordeaux-Sud Owest
开课时间: 2008-08-29
课程语种: 英语
中文简介:
机器学习和统计中最常见的问题之一是从观测向量y估计平均响应Xβ,假设y=Xβ+ε,其中X是已知的,β是感兴趣的参数向量,ε是随机误差向量。我们在这里特别感兴趣的是,β的维数K远高于y的维数。我们提出了一些灵活的贝叶斯模型,可以生成β的稀疏估计。我们表明,当K趋于无穷大时,这些模型与一类利维过程密切相关。仿真结果表明,我们的模型在一系列流行的替代方案中表现出色。
课程简介: One of the most common problems in machine learning and statistics consists of estimating the mean response X.beta from a vector of observations y assuming y=X.beta+epsilon where X is known, beta is a vector of parameters of interest and epsilon a vector of stochastic errors. We are particularly interested here in the case where the dimension K of beta is much higher than the dimension of y. We propose some flexible Bayesian models which can yield sparse estimates of beta. We show that as K tends to infinity, these models are closely related to a class of Levy processes. Simulations demonstrate that our models outperform significantly a range of popular alternatives.
关 键 词: 机器学习; 参数向量; 稀疏估计
课程来源: 视频讲座网
数据采集: 2023-03-10:chenjy
最后编审: 2023-03-10:chenjy
阅读次数: 21