.png){kind=icon}
返回
Bayesian Random Vector Functional-Link Networks for Robust Data Modeling
.png){kind=icon}
PRE
AI
DOI：10.1109/TCYB.2017.2726143.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Random vector functional-link (RVFL) networks are randomized multilayer perceptrons with a single hidden layer and a linear output layer, which can be trained by solving a linear modeling problem. In particular, they are generally trained using a closed-form solution of the (regularized) least-squares approach. This paper introduces several alternative strategies for performing full Bayesian inference (BI) of RVFL networks. Distinct from standard or classical approaches, our proposed Bayesian training algorithms allow to derive an entire probability distribution over the optimal output weights of the network, instead of a single pointwise estimate according to some given criterion (e.g., least-squares). This provides several known advantages, including the possibility of introducing additional prior knowledge in the training process, the availability of an uncertainty measure during the test phase, and the capability of automatically inferring hyper-parameters from given data. In this paper, two BI algorithms for regression are first proposed that, under some practical assumptions, can be implemented by a simple iterative process with closed-form computations. Simulation results show that one of the proposed algorithms, Bayesian RVFL, is able to outperform standard training algorithms for RVFL networks with a proper regularization factor selected carefully via a line search procedure. A general strategy based on variational inference is also presented, with an application to data modeling problems with noisy outputs or outliers. As we discuss in this paper, using recent advances in automatic differentiation this strategy can be applied to a wide range of additional situations in an immediate fashion.
Keyword:
Bayesian inference (BI)
random vector functional-link (RVFL)
relevance vector machine (RVM)
variational inference
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:
10.5
论文数:
1.1W
被引数:
5.0W
机构
引用论文
Imine-linked micron-network polymers with high polyethylene glycol uptake for shaped-stabilized phase change materials.png)
RSC Advances
RSC AdvancesIF0
PREAI
分享收藏
Particle size estimate of grinding processes using random vector functional link networks with improved robustnessNEUROCOMPUTING
NEUROCOMPUTINGIF6.5
PREAI
分享收藏分享收藏
查看更多内容