Least Squares

Harter, H. Leon

doi:10.1002/0471667196.ess1419

Cited by 2 publications

(3 citation statements)

References 18 publications

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“…Linear least square regression is an estimation method that was developed to approximate an unknown data value within specific boundary (Stigler, ; Harter, ). Generally, linear least square regression is widely used to approximate short‐range process that is fundamentally linear or has linear relationship (Schneider et al ., ).…”

Section: Existing Solution To Estimate the Snr Valuementioning

confidence: 99%

“…The method is known as nonlinear least square regression (NLLSR). The foundational basis of this method begins with the formulation of linear least square regression independently developed by Karl Friedrich Gauss and Adrien‐Marie Legendre traced in Stigler () and Harter (). Later on, the relationship of nonlinear regression with respect to linear least square regression is introduced by Gallant ().…”

Section: Introductionmentioning

confidence: 99%

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Nonlinear least squares regression for single image scanning electron microscope signal‐to‐noise ratio estimation

Sim

NorHisham

2016

Journal of Microscopy

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A new method based on nonlinear least squares regression (NLLSR) is formulated to estimate signal-to-noise ratio (SNR) of scanning electron microscope (SEM) images. The estimation of SNR value based on NLLSR method is compared with the three existing methods of nearest neighbourhood, first-order interpolation and the combination of both nearest neighbourhood and first-order interpolation. Samples of SEM images with different textures, contrasts and edges were used to test the performance of NLLSR method in estimating the SNR values of the SEM images. It is shown that the NLLSR method is able to produce better estimation accuracy as compared to the other three existing methods. According to the SNR results obtained from the experiment, the NLLSR method is able to produce approximately less than 1% of SNR error difference as compared to the other three existing methods.

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Section: Existing Solution To Estimate the Snr Valuementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Nonlinear least squares regression for single image scanning electron microscope signal‐to‐noise ratio estimation

Sim

NorHisham

2016

Journal of Microscopy

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show abstract

“…For example, the least squares loss for regression was already used by Legendre, Gauss, and Adrain in the early 19th century (see, e.g., Harter, 1983;Stigler, 1981; and the references therein), and the classification loss function dates back to the beginning of machine learning.…”

Section: Further Reading and Advanced Topicsmentioning

confidence: 99%

Support Vector Machines

Huang¹

2008

Information Science and Statistics

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Least Squares

Cited by 2 publications

References 18 publications

Nonlinear least squares regression for single image scanning electron microscope signal‐to‐noise ratio estimation

Nonlinear least squares regression for single image scanning electron microscope signal‐to‐noise ratio estimation

Support Vector Machines

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