Sequential Bayesian kernel regression

被引：0

作者：

Vermaak, J ^{[1
]}

Godsill, SJ ^{[1
]}

Doucet, A ^{[1
]}

机构：

[1] Univ Cambridge, Dept Engn, Cambridge CB2 1PZ, England

来源：

ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 16 | 2004年 / 16卷

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We propose a method for sequential Bayesian kernel regression. As is the case for the popular Relevance Vector Machine (RVM) [10, 11], the method automatically identifies the number and locations of the kernels. Our algorithm overcomes some of the computational difficulties related to batch methods for kernel regression. It is non-iterative, and requires only a single pass over the data. It is thus applicable to truly sequential data sets and batch data sets alike. The algorithm is based on a generalisation of Importance Sampling, which allows the design of intuitively simple and efficient proposal distributions for the model parameters. Comparative results on two standard data sets show our algorithm to compare favourably with existing batch estimation strategies.

引用

页码：113 / 120

页数：8

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