.png){kind=icon}
返回
Kernels, regularization and differential equations
.png){kind=icon}
PRE
AI
DOI：10.1016/j.patcog.2008.06.011.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Many common machine learning methods such as support vector machines or Gaussian process inference make use of positive definite kernels, reproducing kernel Hilbert spaces, Gaussian processes, and regularization operators. In this work these objects are presented in a general, unifying framework and interrelations are highlighted. With this in mind we then show how linear stochastic differential equation models can be incorporated naturally into the kernel framework. And vice vel-Sa, many kernel machines can be interpreted in terms of differential equations. We focus especially on ordinary differential equations, also known as dynamical systems, and it is shown that standard kernel inference algorithms are equivalent to Kalman filter methods based on such models. In order not to cloud qualitative insights with heavy mathematical machinery, we restrict Ourselves to finite domains, implying that differential equations are treated via their corresponding finite difference equations. (C) 2008 Elsevier Ltd. All rights reserved.
Keyword:
positive definite kernel
differential equation
Gaussian process
reproducing kernel Hilbert space
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
7.6
论文数:
1.3W
被引数:
4.5W
机构
引用论文
.png){kind=icon}
分享收藏On the Development and Applications of Cellulosic Nanofibrillar and Nanocrystalline Materials纤维素纳米原纤和纳米晶材料的发展与应用
PREAI
分享收藏
Mechanical characterization of neural electrodes based on PDMS-parylene C-PDMS sandwiched system
PREAI
分享收藏
查看更多内容