A Study on Bayesian Learning of One-Dimensional Linear Dynamical Systems

被引：0

作者：

Naito, Takuto ^{[1
]}

Yamazaki, Keisuke ^{[2
]}

机构：

[1] Tokyo Inst Technol, Dept Computat Intelligence & Syst Sci, Midori Ku, R2-5,4259 Nagatsuta, Kanagawa, Japan

[2] Tokyo Inst Technol, Precis & Intelligence Lab, Tokyo, Japan

来源：

NEURAL INFORMATION PROCESSING, PT 1, PROCEEDINGS | 2009年 / 5863卷

关键词：

Kalman Filter; Bayesian Learning; Time-Series Data Analysis;

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：

Linear dynamical systems are widely used in such fields as system control and time-dependent data analysis. Such a system can be regarded as a statistical parametric model; where the coefficients of the state space equations are unknown and given as parameters. The properties of parameter learning have not yet been established; in spite of a wide range of applications. Therefore, this paper investigates the system from the viewpoint of learning theory. It is revealed that the system has singularities in the parameter space. The generalization error measured by the prediction accuracy for unseen data sequences is reduced, due to the presence of these singularities.

引用

页码：110 / +

页数：2

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