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sigmoidal function  (OriginLab corp)


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    OriginLab corp sigmoidal function
    Sigmoidal Function, 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)/sigmoidal+function/10__1074_slash_jbc__m504770200-121-29-10
    Average 90 stars, based on 1 article reviews
    sigmoidal function - by Bioz Stars, 2026-10
    90/100 stars

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    Related Articles

    Inhibition:

    Article Title: Copper complexation of methanobactin isolated from Methylosinus trichosporium OB3b: pH-dependent speciation and modeling.
    Article Snippet: Contents lists available at SciVerse ScienceDirect Journal of Inorganic Biochemistry j ourna l homepage: www.e lsev ie r .com/ locate / j inorgb io Copper complexation of methanobactin isolated from Methylosinus trichosporium OB3b: pH-dependent speciation and modeling Marie-Laure Pesch a, Iso Christl a,⁎, Martin Hoffmann a, Stephan M. Kraemer b, Ruben Kretzschmar a a Institute of Biogeochemistry and Pollutant Dynamics, Department of Environmental Systems Science, ETH Zurich, CHN, Universitätstrasse 16, 8092 Zurich, Switzerland b Department of Environmental Geosciences, University of Vienna, Althanstrasse 14, 1090 Vienna, Austria ⁎ Corresponding author.. Tel.. : +41 44 633 6001; fax: E-mail address: iso.christl@env.ethz.ch (I. Christl).

    Article Title: Existing drugs as broad-spectrum and potent inhibitors for Zika virus by targeting NS2B-NS3 interaction
    Article Snippet: Sigmoidal function within the Origin Suite 6.0 (Origin Lab, Wellesley Hills, MA, USA) was used for dose-responsive curve fittings, unless otherwise specified.

    Article Title: The Environmental-Endocrine Basis of Gynandromorphism (Intersex) in a Crustacean
    Article Snippet: The concentration-response curves were fitted to the data using a sigmoidal function with Origin software (OriginLab Corp. Northampton, MA, USA).

    Article Title: Investigating fibroblast cells under "safe" and "injurious" blue-light exposure by holographic microscopy.
    Article Snippet: It is well known that visible wavelengths, ranging from 400 nm to 700 nm, exert various biological effects, including erythema [1], pigmentation [2–3] and generation of oxygen species [4].. However, nowadays, the interaction of visible light with living cells is still of great interest for a wide variety of purposes [5–8].. Among a plethora of cell features used for investigating cell damages, the morphology and volume are gaining great significance.

    Article Title: The crowd you're in with: effects of different types of crowding agents on protein aggregation.
    Article Snippet: 25 26 27 28 29 30 31 32 33 34 35 Article history: Received 21 September 2013 Received in revised form 1 November 2013 Accepted 11 November 2013 Available online xxxx

    Article Title: Ligand-induced Dimer-Tetramer Transition during the Activation of the Cell Surface Epidermal Growth Factor Receptor-A Multidimensional Microscopy Analysis
    Article Snippet: The data were fitted to a sigmoidal function using Origin (OriginLab). mEGF, or the K35-EGF, at U G I SE R V E I D A D Q U ISIC IO N S on M arch 27, 2015 http://w w w .jbc.org/ D ow nloaded from Alexa 555-K35EGF, and FITC-K35EGF analogs were recovered from micropreparative RP-HPLC immediately prior to assay for quantitation and mitogenic assay (43).

    Article Title: Surfaces for tuning of oligonucleotide biosensing selectivity based on surface-initiated atom transfer radical polymerization on glass and silicon substrates.
    Article Snippet: Covalently immobilized mixed films of oligonucleotide and oligomer components on glass and silicon surfaces are reported.. This work has investigated how such films can improve selectivity for the detection of multiple base-pair mismatches.. The intention was to introduce a “matrix isolation” effect on oligonucleotide probe molecules by surrounding the probes with oligomers, thereby reducing oligonucleotide-to-oligonucleotide and/or oligonucleotide-to-surface interactions.

    Article Title: Connexin hemichannel inhibition ameliorates epidermal pathology in a mouse model of keratitis ichthyosis deafness syndrome
    Article Snippet: Percent inhibition was plotted against drug concentration and fit to a sigmoidal function (OriginLab, Northampton, MA) to determine the IC 50 .

    Concentration Assay:

    Article Title: Copper complexation of methanobactin isolated from Methylosinus trichosporium OB3b: pH-dependent speciation and modeling.
    Article Snippet: Contents lists available at SciVerse ScienceDirect Journal of Inorganic Biochemistry j ourna l homepage: www.e lsev ie r .com/ locate / j inorgb io Copper complexation of methanobactin isolated from Methylosinus trichosporium OB3b: pH-dependent speciation and modeling Marie-Laure Pesch a, Iso Christl a,⁎, Martin Hoffmann a, Stephan M. Kraemer b, Ruben Kretzschmar a a Institute of Biogeochemistry and Pollutant Dynamics, Department of Environmental Systems Science, ETH Zurich, CHN, Universitätstrasse 16, 8092 Zurich, Switzerland b Department of Environmental Geosciences, University of Vienna, Althanstrasse 14, 1090 Vienna, Austria ⁎ Corresponding author.. Tel.. : +41 44 633 6001; fax: E-mail address: iso.christl@env.ethz.ch (I. Christl).

    Article Title: Existing drugs as broad-spectrum and potent inhibitors for Zika virus by targeting NS2B-NS3 interaction
    Article Snippet: Sigmoidal function within the Origin Suite 6.0 (Origin Lab, Wellesley Hills, MA, USA) was used for dose-responsive curve fittings, unless otherwise specified.

    Article Title: The Environmental-Endocrine Basis of Gynandromorphism (Intersex) in a Crustacean
    Article Snippet: The concentration-response curves were fitted to the data using a sigmoidal function with Origin software (OriginLab Corp. Northampton, MA, USA).

    Article Title: Investigating fibroblast cells under "safe" and "injurious" blue-light exposure by holographic microscopy.
    Article Snippet: It is well known that visible wavelengths, ranging from 400 nm to 700 nm, exert various biological effects, including erythema [1], pigmentation [2–3] and generation of oxygen species [4].. However, nowadays, the interaction of visible light with living cells is still of great interest for a wide variety of purposes [5–8].. Among a plethora of cell features used for investigating cell damages, the morphology and volume are gaining great significance.

    Article Title: The crowd you're in with: effects of different types of crowding agents on protein aggregation.
    Article Snippet: 25 26 27 28 29 30 31 32 33 34 35 Article history: Received 21 September 2013 Received in revised form 1 November 2013 Accepted 11 November 2013 Available online xxxx

    Article Title: Ligand-induced Dimer-Tetramer Transition during the Activation of the Cell Surface Epidermal Growth Factor Receptor-A Multidimensional Microscopy Analysis
    Article Snippet: The data were fitted to a sigmoidal function using Origin (OriginLab). mEGF, or the K35-EGF, at U G I SE R V E I D A D Q U ISIC IO N S on M arch 27, 2015 http://w w w .jbc.org/ D ow nloaded from Alexa 555-K35EGF, and FITC-K35EGF analogs were recovered from micropreparative RP-HPLC immediately prior to assay for quantitation and mitogenic assay (43).

    Article Title: Surfaces for tuning of oligonucleotide biosensing selectivity based on surface-initiated atom transfer radical polymerization on glass and silicon substrates.
    Article Snippet: Covalently immobilized mixed films of oligonucleotide and oligomer components on glass and silicon surfaces are reported.. This work has investigated how such films can improve selectivity for the detection of multiple base-pair mismatches.. The intention was to introduce a “matrix isolation” effect on oligonucleotide probe molecules by surrounding the probes with oligomers, thereby reducing oligonucleotide-to-oligonucleotide and/or oligonucleotide-to-surface interactions.

    Article Title: Connexin hemichannel inhibition ameliorates epidermal pathology in a mouse model of keratitis ichthyosis deafness syndrome
    Article Snippet: Percent inhibition was plotted against drug concentration and fit to a sigmoidal function (OriginLab, Northampton, MA) to determine the IC 50 .

    Software:

    Article Title: Copper complexation of methanobactin isolated from Methylosinus trichosporium OB3b: pH-dependent speciation and modeling.
    Article Snippet: Contents lists available at SciVerse ScienceDirect Journal of Inorganic Biochemistry j ourna l homepage: www.e lsev ie r .com/ locate / j inorgb io Copper complexation of methanobactin isolated from Methylosinus trichosporium OB3b: pH-dependent speciation and modeling Marie-Laure Pesch a, Iso Christl a,⁎, Martin Hoffmann a, Stephan M. Kraemer b, Ruben Kretzschmar a a Institute of Biogeochemistry and Pollutant Dynamics, Department of Environmental Systems Science, ETH Zurich, CHN, Universitätstrasse 16, 8092 Zurich, Switzerland b Department of Environmental Geosciences, University of Vienna, Althanstrasse 14, 1090 Vienna, Austria ⁎ Corresponding author.. Tel.. : +41 44 633 6001; fax: E-mail address: iso.christl@env.ethz.ch (I. Christl).

    Article Title: Existing drugs as broad-spectrum and potent inhibitors for Zika virus by targeting NS2B-NS3 interaction
    Article Snippet: Sigmoidal function within the Origin Suite 6.0 (Origin Lab, Wellesley Hills, MA, USA) was used for dose-responsive curve fittings, unless otherwise specified.

    Article Title: The Environmental-Endocrine Basis of Gynandromorphism (Intersex) in a Crustacean
    Article Snippet: The concentration-response curves were fitted to the data using a sigmoidal function with Origin software (OriginLab Corp. Northampton, MA, USA).

    Article Title: Investigating fibroblast cells under "safe" and "injurious" blue-light exposure by holographic microscopy.
    Article Snippet: It is well known that visible wavelengths, ranging from 400 nm to 700 nm, exert various biological effects, including erythema [1], pigmentation [2–3] and generation of oxygen species [4].. However, nowadays, the interaction of visible light with living cells is still of great interest for a wide variety of purposes [5–8].. Among a plethora of cell features used for investigating cell damages, the morphology and volume are gaining great significance.

    Article Title: The crowd you're in with: effects of different types of crowding agents on protein aggregation.
    Article Snippet: 25 26 27 28 29 30 31 32 33 34 35 Article history: Received 21 September 2013 Received in revised form 1 November 2013 Accepted 11 November 2013 Available online xxxx

    Article Title: Ligand-induced Dimer-Tetramer Transition during the Activation of the Cell Surface Epidermal Growth Factor Receptor-A Multidimensional Microscopy Analysis
    Article Snippet: The data were fitted to a sigmoidal function using Origin (OriginLab). mEGF, or the K35-EGF, at U G I SE R V E I D A D Q U ISIC IO N S on M arch 27, 2015 http://w w w .jbc.org/ D ow nloaded from Alexa 555-K35EGF, and FITC-K35EGF analogs were recovered from micropreparative RP-HPLC immediately prior to assay for quantitation and mitogenic assay (43).

    Article Title: Surfaces for tuning of oligonucleotide biosensing selectivity based on surface-initiated atom transfer radical polymerization on glass and silicon substrates.
    Article Snippet: Covalently immobilized mixed films of oligonucleotide and oligomer components on glass and silicon surfaces are reported.. This work has investigated how such films can improve selectivity for the detection of multiple base-pair mismatches.. The intention was to introduce a “matrix isolation” effect on oligonucleotide probe molecules by surrounding the probes with oligomers, thereby reducing oligonucleotide-to-oligonucleotide and/or oligonucleotide-to-surface interactions.

    Article Title: Connexin hemichannel inhibition ameliorates epidermal pathology in a mouse model of keratitis ichthyosis deafness syndrome
    Article Snippet: Percent inhibition was plotted against drug concentration and fit to a sigmoidal function (OriginLab, Northampton, MA) to determine the IC 50 .



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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: