Sensor drift detection in SNG plant using auto-associative kernel regression

2017 ◽  
Author(s):  
Jae-Min cha ◽  
Taekyoung Lee ◽  
Joon-Young Kim ◽  
Junguk Shin ◽  
Jinil Kim ◽  
...  
Keyword(s):  
Kernel Regression ◽  
Sensor Drift

scholarly journals Drift Compensation on Massive Online Electronic-Nose Responses

Chemosensors ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 78
Author(s):  
Jianhua Cao ◽  
Tao Liu ◽  
Jianjun Chen ◽  
Tao Yang ◽  
Xiuxiu Zhu ◽  
...  
Keyword(s):  
Active Learning ◽  
Gas Sensor ◽  
Electronic Nose ◽  
Effective Means ◽  
Learning Paradigm ◽  
Class Label ◽  
Learning Framework ◽  
Sensor Drift ◽  
Drift Compensation ◽  
Noisy Labels

Gas sensor drift is an important issue of electronic nose (E-nose) systems. This study follows this concern under the condition that requires an instant drift compensation with massive online E-nose responses. Recently, an active learning paradigm has been introduced to such condition. However, it does not consider the “noisy label” problem caused by the unreliability of its labeling process in real applications. Thus, we have proposed a class-label appraisal methodology and associated active learning framework to assess and correct the noisy labels. To evaluate the performance of the proposed methodologies, we used the datasets from two E-nose systems. The experimental results show that the proposed methodology helps the E-noses achieve higher accuracy with lower computation than the reference methods do. Finally, we can conclude that the proposed class-label appraisal mechanism is an effective means of enhancing the robustness of active learning-based E-nose drift compensation.

Large sample results for varying kernel regression estimates

2013 ◽  
Vol 25 (4) ◽  
pp. 829-853 ◽  
Author(s):  
Hira L. Koul ◽  
Weixing Song
Keyword(s):  
Kernel Regression ◽  
Large Sample

scholarly journals Spectral bias and task-model alignment explain generalization in kernel regression and infinitely wide neural networks

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Abdulkadir Canatar ◽  
Blake Bordelon ◽  
Cengiz Pehlevan
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Kernel Regression ◽  
Learning Task ◽  
Learning Curves ◽  
Generalization Error ◽  
Theoretical Understanding ◽  
Classical Statistics ◽  
Deep Networks ◽  
Model Alignment

AbstractA theoretical understanding of generalization remains an open problem for many machine learning models, including deep networks where overparameterization leads to better performance, contradicting the conventional wisdom from classical statistics. Here, we investigate generalization error for kernel regression, which, besides being a popular machine learning method, also describes certain infinitely overparameterized neural networks. We use techniques from statistical mechanics to derive an analytical expression for generalization error applicable to any kernel and data distribution. We present applications of our theory to real and synthetic datasets, and for many kernels including those that arise from training deep networks in the infinite-width limit. We elucidate an inductive bias of kernel regression to explain data with simple functions, characterize whether a kernel is compatible with a learning task, and show that more data may impair generalization when noisy or not expressible by the kernel, leading to non-monotonic learning curves with possibly many peaks.

Kernel Regression on Graphs in Random Fourier Features Space

2021 ◽  
Author(s):  
Vitor R. M. Elias ◽  
Vinay C. Gogineni ◽  
Wallace A. Martins ◽  
Stefan Werner
Keyword(s):  
Kernel Regression ◽  
Random Fourier Features

Single and mixed effects of metallic elements in maternal serum during pregnancy on risk for fetal neural tube defects: A Bayesian kernel regression approach

2021 ◽  
Vol 285 ◽  
pp. 117203
Author(s):  
Tian Tian ◽  
Shengju Yin ◽  
Lei Jin ◽  
Jufen Liu ◽  
Chengrong Wang ◽  
...  
Keyword(s):  
Neural Tube ◽  
Neural Tube Defects ◽  
Kernel Regression ◽  
Mixed Effects ◽  
Maternal Serum ◽  
Metallic Elements ◽  
Regression Approach

Constrained kernel regression for pose estimation

2014 ◽  
Vol 50 (2) ◽  
pp. 77-79 ◽  
Author(s):  
Haopeng Zhang ◽  
Zhiguo Jiang
Keyword(s):  
Pose Estimation ◽  
Kernel Regression

Single image super-resolution using regularization of non-local steering kernel regression

2016 ◽  
Vol 123 ◽  
pp. 53-63 ◽  
Author(s):  
Kaibing Zhang ◽  
Xinbo Gao ◽  
Jie Li ◽  
Hongxing Xia
Keyword(s):  
Kernel Regression ◽  
Super Resolution ◽  
Single Image ◽  
Non Local ◽  
Image Super Resolution ◽  
Single Image Super Resolution
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