Data from: A novel algorithm to enhance P300 in single trials: application to lie detection using F-score and SVM

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关键词：

EEG;P300;ICA;EEG signal processing;lie detection;biomedical engineer;F-score_SVM

DOI：

doi:10.5061/dryad.2qc64

摘要：

nce comparison. The optimal parameter values in the SDA and the classifiers were tuned using a grid-searching training procedure with cross-validation. The support vector machine

Data from: Development of machine learning models for diagnosis of glaucoma

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关键词：

cornea thickness;RNFL;glaucoma;ocular pressure

DOI：

doi:10.5061/dryad.q6ft5

摘要：

on model, we considered four machine learning algorithms: C5.0, random forest (RF), support vector machine (SVM), and k-nearest neighbor (KNN). We repeatedly compo

Patchy promiscuity: machine learning applied to predict the host specificity of Salmonella enterica and Escherichia coli

负责人：

University Of Edinburgh;;University Of Edinburgh

关键词：

Biological Sciences::Medical and Veterinary Microbiology

DOI：

doi:10.7488/ds/2102

摘要：

Support Vector Machine (SVM) classifiers were built based on whole genome sequence content. Analysis of over 1000 S. enterica genomes allowed the correct prediction (67% - 90

Data from: Comparing radiomic classifiers and classifier ensembles for detection of peripheral zone prostate tumors on T2-weighted MRI: a multi-si

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关键词：

Radiomics;comparison;texture;classifiers;Machine Learning;medical imaging

DOI：

doi:10.5061/dryad.026cj63

摘要：

-weighted (T2w) MRI. Methods: 12 different supervised classifier schemes: Quadratic Discriminant Analysis (QDA), Support Vector Machines

Data from: Pain intensity recognition rates via biopotential feature patterns with support vector machines

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关键词：

Holy Grail;BioVid Heat Pain Database;Biopotential Feature List

DOI：

doi:10.5061/dryad.2b09s

摘要：

arity, variability, and similarity. Results: We achieved classification rates of 90.94% for baseline vs. pain tolerance threshold and 79.29% for baseline

Land cover change maps for Mato Grosso State in Brazil: 2001-2016, links to files, supplement to: Picoli, Michelle; Camara, Gilberto; Sanches, Ieda

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关键词：

File content File name File format File size Uniform resource locator\/link to file Multiple investigations

DOI：

doi:10.1594/pangaea.881291

摘要：

for a support vector machine classifier. The classes include natural and human-transformed land areas, discriminating among different agricultural crops in state of Mato Gross

Data from: Mathematical modeling for the prediction of cerebral white matter lesions based on clinical examination data

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关键词：

2016-2017;cerebral white matter lesions;clinical examination data;mathematical modeling;Homo Sapiens

DOI：

doi:10.5061/dryad.73bh2q8

摘要：

s, logistic discrimination, Naive Bayes classifier, support vector machine, and random forest were investigated and evaluated by ten-fold cross-validation, usi