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Several complexity measures for supervised classification have been designed to define these areas.
In this paper, we want to discover which type of datasets, defined by certain range values of the complexity measures for supervised classification, fits for some of the most well-known semi-naive Bayesian network classifiers.
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Therefore, we use different groups of topological network measures as input for supervised machine learning techniques in order to comprehensively capture topological network properties for classification.
The comparison of the performance in Tables 4 and 5, reveals that due to the introduction of more training data, there is bigger improvement on the test set for supervised classifiers in both F1 measures, in particular for SVM and Logistic regression, while Semi-supervised RDE showed much more robust performance on the two different sets.
DeMint introduced a measure (which ultimately failed) that would have banned federal funding for supervised injection facilities.
This method combines supervised learning and statistical measures for the chosen candidate SNPs and/or environmental variables to reconcile redundancy information for improving the classification and prediction performance.
Liu et al. [ 20] developed a feature selection method to combines supervised learning and statistical measures for the chosen candidate features/SNPs to reconcile the redundancy information and, in doing so, improve the classification performance in association studies.
This method combines supervised learning and statistical measures for the chosen candidate features/SNPs to reconcile the redundancy information and, in doing so, improve the classification performance in association studies.
Currently, Medicare provides limited benefits for rehabilitation, and funding for supervised therapy is especially meagre.
only output PEs are supplied for supervised training.
They can be used for supervised and unsupervised classification.
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