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boltzmann sigmoidal functions  (OriginLab corp)


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    Structured Review

    OriginLab corp boltzmann sigmoidal functions
    Boltzmann Sigmoidal Functions, supplied by OriginLab corp, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/activation+function+(sigmoidal+function)/boltzmann+sigmoidal+function/pm29886032-74-18-23
    Average 90 stars, based on 1 article reviews
    boltzmann sigmoidal functions - by Bioz Stars, 2026-09
    90/100 stars

    Images

    Related Articles

    Purification:

    Article Title: Engineering of a hydroxysteroid dehydrogenase with simultaneous enhancement in activity and thermostability for efficient biosynthesis of ursodeoxycholic acid.
    Article Snippet: Hydroxysteroid dehydrogenases (HSDHs) catalyze the oxidation/reduction of hydroxyl/keto groups of steroids with high regioor stereoselectivity, playing an essential role in producing optically pure chemicals.. In this work, a novel approach was developed to simultaneously improve the stability and activity of 7β-hydroxyste roid dehydrogenase (7β-HSDH) by combining B-factor analysis and computer-aided prediction.. Several advantageous mutants were identified, and the most promising variant, S51Y/P202Y, exhibited 2.3-fold improvements in catalytic activity, 3.3-fold in half-life at 40°C, and 4.7-fold in catalytic efficiency (kcat/Km), respectively.

    Incubation:

    Article Title: Engineering of a hydroxysteroid dehydrogenase with simultaneous enhancement in activity and thermostability for efficient biosynthesis of ursodeoxycholic acid.
    Article Snippet: Hydroxysteroid dehydrogenases (HSDHs) catalyze the oxidation/reduction of hydroxyl/keto groups of steroids with high regioor stereoselectivity, playing an essential role in producing optically pure chemicals.. In this work, a novel approach was developed to simultaneously improve the stability and activity of 7β-hydroxyste roid dehydrogenase (7β-HSDH) by combining B-factor analysis and computer-aided prediction.. Several advantageous mutants were identified, and the most promising variant, S51Y/P202Y, exhibited 2.3-fold improvements in catalytic activity, 3.3-fold in half-life at 40°C, and 4.7-fold in catalytic efficiency (kcat/Km), respectively.

    Activity Assay:

    Article Title: Engineering of a hydroxysteroid dehydrogenase with simultaneous enhancement in activity and thermostability for efficient biosynthesis of ursodeoxycholic acid.
    Article Snippet: Hydroxysteroid dehydrogenases (HSDHs) catalyze the oxidation/reduction of hydroxyl/keto groups of steroids with high regioor stereoselectivity, playing an essential role in producing optically pure chemicals.. In this work, a novel approach was developed to simultaneously improve the stability and activity of 7β-hydroxyste roid dehydrogenase (7β-HSDH) by combining B-factor analysis and computer-aided prediction.. Several advantageous mutants were identified, and the most promising variant, S51Y/P202Y, exhibited 2.3-fold improvements in catalytic activity, 3.3-fold in half-life at 40°C, and 4.7-fold in catalytic efficiency (kcat/Km), respectively.

    Inhibition:

    Article Title: Preclinical Studies and Drug Combination of Low-Cost Molecules for Chagas Disease
    Article Snippet: .. This is determined by plotting the % inhibition against the log 10 of the concentration and fitting the points to a sigmoid Boltzmann curve (dose–response curve) using OriginLab 8.5. ..

    Concentration Assay:

    Article Title: Preclinical Studies and Drug Combination of Low-Cost Molecules for Chagas Disease
    Article Snippet: .. This is determined by plotting the % inhibition against the log 10 of the concentration and fitting the points to a sigmoid Boltzmann curve (dose–response curve) using OriginLab 8.5. ..

    Protein Concentration:

    Article Title: The feruloyl esterase from Thermobacillus xylanilyticus shows broad specificity for processing pre-biotic feruloylated xylooligosaccharides at high temperatures.
    Article Snippet: Ferulic acid has antioxidant properties of interest to the food industry and can be released from natural plant fibres using feruloyl esterases.. Esterases active at high temperatures are highly desirable but currently underrepresented.. Here we report the biochemical characterization of the feruloyl esterase from Thermobacillus xylanilyticus.

    Software:

    Article Title: Physico-chemical characterization of bilirubin-10-sulfonate and comparison of its acid–base behavior with unconjugated bilirubin
    Article Snippet: .. The position of the inflection points was determined using the numerical interpolation procedure using the standard Boltzmann sigmoidal function defined in the Origin software (Origin (Pro), Version 2016, OriginLab Corporation, Northampton, MA, USA Corporation, MA USA). ..

    other:

    Article Title: I-motif formation in the promoter region of the B-MYB proto-oncogene.
    Article Snippet: The data were normalized from 0 to 1 and the Boltzmann sigmoidal function was employed using OriginPro 2021 (OriginLab Corporation, Northampton, Massachusetts, USA).



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    k GPRELM’s overfitting (for a fixed <t>\documentclass[12pt]{minimal}</t> \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\lambda =1$$\end{document} λ = 1 )
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    Image Search Results


    k GPRELM’s overfitting (for a fixed \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\lambda =1$$\end{document} λ = 1 )

    Journal: Knowledge and Information Systems

    Article Title: A novel correlation Gaussian process regression-based extreme learning machine

    doi: 10.1007/s10115-022-01803-4

    Figure Lengend Snippet: k GPRELM’s overfitting (for a fixed \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\lambda =1$$\end{document} λ = 1 )

    Article Snippet: This is caused by the mathematical property of the sigmoid activation function \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$g\left( v \right) = \frac{1}{{1 + e^{ - v} }}$$\end{document} g v = + e - v . In MATLAB, when v \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\le $$\end{document} ≤ \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$-12.206$$\end{document} - 12.206 , \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$g\left( v \right) $$\end{document} g v \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\le $$\end{document} ≤ 0.00001; when v \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\ge $$\end{document} ≥ 11.108, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$g\left( v \right) $$\end{document} g v \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\ge $$\end{document} ≥ 0.99999.

    Techniques:

    Main differences between ELM, k GPRELM, and c GPRELM

    Journal: Knowledge and Information Systems

    Article Title: A novel correlation Gaussian process regression-based extreme learning machine

    doi: 10.1007/s10115-022-01803-4

    Figure Lengend Snippet: Main differences between ELM, k GPRELM, and c GPRELM

    Article Snippet: This is caused by the mathematical property of the sigmoid activation function \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$g\left( v \right) = \frac{1}{{1 + e^{ - v} }}$$\end{document} g v = + e - v . In MATLAB, when v \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\le $$\end{document} ≤ \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$-12.206$$\end{document} - 12.206 , \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$g\left( v \right) $$\end{document} g v \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\le $$\end{document} ≤ 0.00001; when v \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\ge $$\end{document} ≥ 11.108, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$g\left( v \right) $$\end{document} g v \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\ge $$\end{document} ≥ 0.99999.

    Techniques: Transformation Assay

    Predictive performances of k GPRELM ( \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\big ( {L,\sigma _N ,\lambda ^2 }\big )=\big ({190,2^{- 20},2^{-9} }\big )$$\end{document} ( L , σ N , λ 2 ) = ( 190 , 2 - 20 , 2 - 9 ) ) and c GPRELM ( \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\big ( {L,\sigma _N}\big )=\big ({80,2^{- 20}}\big )$$\end{document} ( L , σ N ) = ( 80 , 2 - 20 ) ) on 200 SinC instances

    Journal: Knowledge and Information Systems

    Article Title: A novel correlation Gaussian process regression-based extreme learning machine

    doi: 10.1007/s10115-022-01803-4

    Figure Lengend Snippet: Predictive performances of k GPRELM ( \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\big ( {L,\sigma _N ,\lambda ^2 }\big )=\big ({190,2^{- 20},2^{-9} }\big )$$\end{document} ( L , σ N , λ 2 ) = ( 190 , 2 - 20 , 2 - 9 ) ) and c GPRELM ( \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\big ( {L,\sigma _N}\big )=\big ({80,2^{- 20}}\big )$$\end{document} ( L , σ N ) = ( 80 , 2 - 20 ) ) on 200 SinC instances

    Article Snippet: This is caused by the mathematical property of the sigmoid activation function \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$g\left( v \right) = \frac{1}{{1 + e^{ - v} }}$$\end{document} g v = + e - v . In MATLAB, when v \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\le $$\end{document} ≤ \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$-12.206$$\end{document} - 12.206 , \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$g\left( v \right) $$\end{document} g v \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\le $$\end{document} ≤ 0.00001; when v \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\ge $$\end{document} ≥ 11.108, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$g\left( v \right) $$\end{document} g v \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\ge $$\end{document} ≥ 0.99999.

    Techniques:

    Maximal training accuracies of ELM, k GPRELM, c GPRELM, and ML-ELM and corresponding testing accuracies

    Journal: Knowledge and Information Systems

    Article Title: A novel correlation Gaussian process regression-based extreme learning machine

    doi: 10.1007/s10115-022-01803-4

    Figure Lengend Snippet: Maximal training accuracies of ELM, k GPRELM, c GPRELM, and ML-ELM and corresponding testing accuracies

    Article Snippet: This is caused by the mathematical property of the sigmoid activation function \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$g\left( v \right) = \frac{1}{{1 + e^{ - v} }}$$\end{document} g v = + e - v . In MATLAB, when v \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\le $$\end{document} ≤ \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$-12.206$$\end{document} - 12.206 , \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$g\left( v \right) $$\end{document} g v \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\le $$\end{document} ≤ 0.00001; when v \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\ge $$\end{document} ≥ 11.108, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$g\left( v \right) $$\end{document} g v \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\ge $$\end{document} ≥ 0.99999.

    Techniques:

    Minimal training RMSEs of ELM, k GPRELM, c GPRELM, and ML-ELM and corresponding testing RMSEs

    Journal: Knowledge and Information Systems

    Article Title: A novel correlation Gaussian process regression-based extreme learning machine

    doi: 10.1007/s10115-022-01803-4

    Figure Lengend Snippet: Minimal training RMSEs of ELM, k GPRELM, c GPRELM, and ML-ELM and corresponding testing RMSEs

    Article Snippet: This is caused by the mathematical property of the sigmoid activation function \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$g\left( v \right) = \frac{1}{{1 + e^{ - v} }}$$\end{document} g v = + e - v . In MATLAB, when v \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\le $$\end{document} ≤ \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$-12.206$$\end{document} - 12.206 , \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$g\left( v \right) $$\end{document} g v \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\le $$\end{document} ≤ 0.00001; when v \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\ge $$\end{document} ≥ 11.108, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$g\left( v \right) $$\end{document} g v \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\ge $$\end{document} ≥ 0.99999.

    Techniques:

    Training and testing times of ELM, k GPRELM, c GPRELM, and ML-ELM on 19 classification data sets

    Journal: Knowledge and Information Systems

    Article Title: A novel correlation Gaussian process regression-based extreme learning machine

    doi: 10.1007/s10115-022-01803-4

    Figure Lengend Snippet: Training and testing times of ELM, k GPRELM, c GPRELM, and ML-ELM on 19 classification data sets

    Article Snippet: This is caused by the mathematical property of the sigmoid activation function \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$g\left( v \right) = \frac{1}{{1 + e^{ - v} }}$$\end{document} g v = + e - v . In MATLAB, when v \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\le $$\end{document} ≤ \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$-12.206$$\end{document} - 12.206 , \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$g\left( v \right) $$\end{document} g v \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\le $$\end{document} ≤ 0.00001; when v \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\ge $$\end{document} ≥ 11.108, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$g\left( v \right) $$\end{document} g v \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\ge $$\end{document} ≥ 0.99999.

    Techniques:

    Training and testing times of ELM, k GPRELM, c GPRELM, and ML-ELM on 10 regression data sets

    Journal: Knowledge and Information Systems

    Article Title: A novel correlation Gaussian process regression-based extreme learning machine

    doi: 10.1007/s10115-022-01803-4

    Figure Lengend Snippet: Training and testing times of ELM, k GPRELM, c GPRELM, and ML-ELM on 10 regression data sets

    Article Snippet: This is caused by the mathematical property of the sigmoid activation function \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$g\left( v \right) = \frac{1}{{1 + e^{ - v} }}$$\end{document} g v = + e - v . In MATLAB, when v \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\le $$\end{document} ≤ \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$-12.206$$\end{document} - 12.206 , \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$g\left( v \right) $$\end{document} g v \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\le $$\end{document} ≤ 0.00001; when v \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\ge $$\end{document} ≥ 11.108, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$g\left( v \right) $$\end{document} g v \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\ge $$\end{document} ≥ 0.99999.

    Techniques:

    Ranks of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathrm{{K}}\left( {\mathrm{{H}},\mathrm{{H}}} \right) + \sigma _N^2 \mathrm{{I}}$$\end{document} K H , H + σ N 2 I and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathrm{{C}}\left( {\mathrm{{H}},\mathrm{{H}}} \right) + \sigma _N^2 \mathrm{{I}}$$\end{document} C H , H + σ N 2 I on two representative classification data sets

    Journal: Knowledge and Information Systems

    Article Title: A novel correlation Gaussian process regression-based extreme learning machine

    doi: 10.1007/s10115-022-01803-4

    Figure Lengend Snippet: Ranks of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathrm{{K}}\left( {\mathrm{{H}},\mathrm{{H}}} \right) + \sigma _N^2 \mathrm{{I}}$$\end{document} K H , H + σ N 2 I and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathrm{{C}}\left( {\mathrm{{H}},\mathrm{{H}}} \right) + \sigma _N^2 \mathrm{{I}}$$\end{document} C H , H + σ N 2 I on two representative classification data sets

    Article Snippet: This is caused by the mathematical property of the sigmoid activation function \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$g\left( v \right) = \frac{1}{{1 + e^{ - v} }}$$\end{document} g v = + e - v . In MATLAB, when v \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\le $$\end{document} ≤ \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$-12.206$$\end{document} - 12.206 , \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$g\left( v \right) $$\end{document} g v \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\le $$\end{document} ≤ 0.00001; when v \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\ge $$\end{document} ≥ 11.108, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$g\left( v \right) $$\end{document} g v \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\ge $$\end{document} ≥ 0.99999.

    Techniques:

    Ranks of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathrm{{K}}\left( {\mathrm{{H}},\mathrm{{H}}} \right) + \sigma _N^2 \mathrm{{I}}$$\end{document} K H , H + σ N 2 I and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathrm{{C}}\left( {\mathrm{{H}},\mathrm{{H}}} \right) + \sigma _N^2 \mathrm{{I}}$$\end{document} C H , H + σ N 2 I on two representative regression data sets

    Journal: Knowledge and Information Systems

    Article Title: A novel correlation Gaussian process regression-based extreme learning machine

    doi: 10.1007/s10115-022-01803-4

    Figure Lengend Snippet: Ranks of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathrm{{K}}\left( {\mathrm{{H}},\mathrm{{H}}} \right) + \sigma _N^2 \mathrm{{I}}$$\end{document} K H , H + σ N 2 I and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathrm{{C}}\left( {\mathrm{{H}},\mathrm{{H}}} \right) + \sigma _N^2 \mathrm{{I}}$$\end{document} C H , H + σ N 2 I on two representative regression data sets

    Article Snippet: This is caused by the mathematical property of the sigmoid activation function \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$g\left( v \right) = \frac{1}{{1 + e^{ - v} }}$$\end{document} g v = + e - v . In MATLAB, when v \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\le $$\end{document} ≤ \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$-12.206$$\end{document} - 12.206 , \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$g\left( v \right) $$\end{document} g v \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\le $$\end{document} ≤ 0.00001; when v \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\ge $$\end{document} ≥ 11.108, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$g\left( v \right) $$\end{document} g v \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\ge $$\end{document} ≥ 0.99999.

    Techniques: