perceptron classifier Search Results


deep multi-layer perceptron classifier  (IEEE Access)
90
IEEE Access deep multi-layer perceptron classifier
Deep Multi Layer Perceptron Classifier, supplied by IEEE Access, 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/deep multi-layer perceptron classifier/product/IEEE Access
Average 90 stars, based on 1 article reviews
deep multi-layer perceptron classifier - by Bioz Stars, 2026-03
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classifiers (multilayer perceptron)  (SoftMax Inc)
90
SoftMax Inc classifiers (multilayer perceptron)
Classifiers (Multilayer Perceptron), supplied by SoftMax 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/classifiers (multilayer perceptron)/product/SoftMax Inc
Average 90 stars, based on 1 article reviews
classifiers (multilayer perceptron) - by Bioz Stars, 2026-03
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voted perceptron (vp) classifier  (KNIME GmbH)
90
KNIME GmbH voted perceptron (vp) classifier
The sequence minimization optimization (SMO) and Random Forest (RF) classifier models showed greater predictive accuracy than the Naïve Bayesian (NB) and Voted <t>Perceptron</t> (VP) classifier models.
Voted Perceptron (Vp) Classifier, supplied by KNIME GmbH, 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/voted perceptron (vp) classifier/product/KNIME GmbH
Average 90 stars, based on 1 article reviews
voted perceptron (vp) classifier - by Bioz Stars, 2026-03
90/100 stars
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Image Search Results


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: