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perceptron classifier  (MathWorks Inc)


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    MathWorks Inc perceptron classifier
    Perceptron Classifier, 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/product/perceptron+classifier/pmc02851246-109-3-15
    Average 90 stars, based on 1 article reviews
    perceptron classifier - by Bioz Stars, 2026-10
    90/100 stars

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    other:

    Article Title: Model calcium sensors for network homeostasis: Sensor and readout parameter analysis from a database of model neuronal networks
    Article Snippet: We employed the perceptron classifier ( Rosenblatt, 1958 ) using the newff function of Matlab (Mathworks, Natick, MA).



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    The sequence minimization optimization (SMO) and Random Forest (RF) classifier models showed greater predictive accuracy than the Naïve Bayesian (NB) and Voted Perceptron (VP) classifier models.

    Journal: PLoS ONE

    Article Title: Predictive classifier models built from natural products with antimalarial bioactivity using machine learning approach

    doi: 10.1371/journal.pone.0204644

    Figure Lengend Snippet: The sequence minimization optimization (SMO) and Random Forest (RF) classifier models showed greater predictive accuracy than the Naïve Bayesian (NB) and Voted Perceptron (VP) classifier models.

    Article Snippet: A metanode in the KNIME workflow ( ) was designed to build the various classifier models that were earlier mentioned (i.e. Naïve Bayesian classifier, Sequential Minimization Optimization (SMO) classifier, Random Forest (RF) classifier and Voted perceptron (VP) classifier).

    Techniques: Sequencing

    Evaluation parameters from the prediction of bioactivity class of an independent NAA test dataset by the four classifier models used in this study.

    Journal: PLoS ONE

    Article Title: Predictive classifier models built from natural products with antimalarial bioactivity using machine learning approach

    doi: 10.1371/journal.pone.0204644

    Figure Lengend Snippet: Evaluation parameters from the prediction of bioactivity class of an independent NAA test dataset by the four classifier models used in this study.

    Article Snippet: A metanode in the KNIME workflow ( ) was designed to build the various classifier models that were earlier mentioned (i.e. Naïve Bayesian classifier, Sequential Minimization Optimization (SMO) classifier, Random Forest (RF) classifier and Voted perceptron (VP) classifier).

    Techniques: Sequencing, Plasmid Preparation

    The diagonal grey line represents classifier models that randomly assign compounds to bioactivity class (and will have an area under the curve (AUC) of 0.5). The blue line shown in the ROC curve of Voted perceptron (will have an AUC of 1.0) represents classifier models that perfectly predict bioactivity class of compounds. The red line is the ROC curve from the predictions by the four classifier models. The area under the ROC curve (AUC), a measure of bioactivity class discriminatory power of a classifier model, is shown on each ROC curve.

    Journal: PLoS ONE

    Article Title: Predictive classifier models built from natural products with antimalarial bioactivity using machine learning approach

    doi: 10.1371/journal.pone.0204644

    Figure Lengend Snippet: The diagonal grey line represents classifier models that randomly assign compounds to bioactivity class (and will have an area under the curve (AUC) of 0.5). The blue line shown in the ROC curve of Voted perceptron (will have an AUC of 1.0) represents classifier models that perfectly predict bioactivity class of compounds. The red line is the ROC curve from the predictions by the four classifier models. The area under the ROC curve (AUC), a measure of bioactivity class discriminatory power of a classifier model, is shown on each ROC curve.

    Article Snippet: A metanode in the KNIME workflow ( ) was designed to build the various classifier models that were earlier mentioned (i.e. Naïve Bayesian classifier, Sequential Minimization Optimization (SMO) classifier, Random Forest (RF) classifier and Voted perceptron (VP) classifier).

    Techniques: