scholarly journals Principal surfaces from unsupervised kernel regression

2005 ◽  
Vol 27 (9) ◽  
pp. 1379-1391 ◽  
Author(s):  
P. Meinicke ◽  
S. Klanke ◽  
R. Memisevic ◽  
H. Ritter
Keyword(s):  
Kernel Regression ◽  
Principal Surfaces

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.

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

Distributed learning for sketched kernel regression

2021 ◽  
Author(s):  
Heng Lian ◽  
Jiamin Liu ◽  
Zengyan Fan
Keyword(s):  
Kernel Regression ◽  
Distributed Learning

scholarly journals Estimating Illumination Chromaticity via Kernel Regression

2006 ◽  
Author(s):  
Vivek Agarwal ◽  
Andrei V. Gribok ◽  
Andreas Koschan ◽  
Mongi A. Abidi
Keyword(s):  
Kernel Regression
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