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Data from: A novel algorithm to enhance P300 in single trials: application to lie detection using F-score and SVM
- 负责人:
- 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
![](http://agri.nais.net.cn/resources/front/images/source_91.jpg)
Data from: Development of machine learning models for diagnosis of glaucoma
- 负责人:
- 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
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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
- 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
![](http://agri.nais.net.cn/resources/front/images/source_91.jpg)
Data from: Comparing radiomic classifiers and classifier ensembles for detection of peripheral zone prostate tumors on T2-weighted MRI: a multi-si
- 负责人:
- DOI:
- doi:10.5061/dryad.026cj63
- 摘要:
- -weighted (T2w) MRI. Methods: 12 different supervised classifier schemes: Quadratic Discriminant Analysis (QDA), Support Vector Machines
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Data from: Pain intensity recognition rates via biopotential feature patterns with support vector machines
- 负责人:
- 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
![](http://agri.nais.net.cn/resources/front/images/source_91.jpg)
Land cover change maps for Mato Grosso State in Brazil: 2001-2016, links to files, supplement to: Picoli, Michelle; Camara, Gilberto; Sanches, Ieda
- 负责人:
- 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
![](http://agri.nais.net.cn/resources/front/images/source_91.jpg)
Data from: Mathematical modeling for the prediction of cerebral white matter lesions based on clinical examination data
- 负责人:
- 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