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classification learner machine learning tool  (MathWorks Inc)


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    Structured Review

    MathWorks Inc classification learner machine learning tool
    (a) Comparison of mean accuracy with standard deviations for the different <t>machine</t> <t>learning</t> <t>classification</t> methods, along with best performing subclass. Confusion matrix plots generated from a single 10 fold cross-validation run using the cubic support vector machine subclass with (b) stomach separated out into fundus, body, and antrum and (c) fundus, body, and antrum aggregated together as stomach prior to comparison to other tissues.
    Classification Learner Machine Learning Tool, supplied by MathWorks Inc, 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/result/classification learner machine learning tool/product/MathWorks Inc
    Average 90 stars, based on 1 article reviews
    classification learner machine learning tool - by Bioz Stars, 2026-03
    90/100 stars

    Images

    1) Product Images from "Electrical Impedance Characterization of in Vivo Porcine Tissue Using Machine Learning"

    Article Title: Electrical Impedance Characterization of in Vivo Porcine Tissue Using Machine Learning

    Journal: Journal of Electrical Bioimpedance

    doi: 10.2478/joeb-2021-0005

    (a) Comparison of mean accuracy with standard deviations for the different machine learning classification methods, along with best performing subclass. Confusion matrix plots generated from a single 10 fold cross-validation run using the cubic support vector machine subclass with (b) stomach separated out into fundus, body, and antrum and (c) fundus, body, and antrum aggregated together as stomach prior to comparison to other tissues.
    Figure Legend Snippet: (a) Comparison of mean accuracy with standard deviations for the different machine learning classification methods, along with best performing subclass. Confusion matrix plots generated from a single 10 fold cross-validation run using the cubic support vector machine subclass with (b) stomach separated out into fundus, body, and antrum and (c) fundus, body, and antrum aggregated together as stomach prior to comparison to other tissues.

    Techniques Used: Comparison, Generated, Biomarker Discovery, Plasmid Preparation



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    (a) Comparison of mean accuracy with standard deviations for the different <t>machine</t> <t>learning</t> <t>classification</t> methods, along with best performing subclass. Confusion matrix plots generated from a single 10 fold cross-validation run using the cubic support vector machine subclass with (b) stomach separated out into fundus, body, and antrum and (c) fundus, body, and antrum aggregated together as stomach prior to comparison to other tissues.
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    Image Search Results


    (a) Comparison of mean accuracy with standard deviations for the different machine learning classification methods, along with best performing subclass. Confusion matrix plots generated from a single 10 fold cross-validation run using the cubic support vector machine subclass with (b) stomach separated out into fundus, body, and antrum and (c) fundus, body, and antrum aggregated together as stomach prior to comparison to other tissues.

    Journal: Journal of Electrical Bioimpedance

    Article Title: Electrical Impedance Characterization of in Vivo Porcine Tissue Using Machine Learning

    doi: 10.2478/joeb-2021-0005

    Figure Lengend Snippet: (a) Comparison of mean accuracy with standard deviations for the different machine learning classification methods, along with best performing subclass. Confusion matrix plots generated from a single 10 fold cross-validation run using the cubic support vector machine subclass with (b) stomach separated out into fundus, body, and antrum and (c) fundus, body, and antrum aggregated together as stomach prior to comparison to other tissues.

    Article Snippet: The validity of utilizing impedance for tissue differentiation was evaluated using the MATLAB classification learner machine learning tool.

    Techniques: Comparison, Generated, Biomarker Discovery, Plasmid Preparation