Macro-F1 results comparison of four widely used machine learning models under seven combinations of preprocessing methods (TQ-tax question dataset; TC-THUCNews).</p
<p>Three classifiers, Gaussian Naive Bayes (GNB) in panel (a), SVM in panel (b) and sparse MRF in pa...
(a) exhibits performance of different deep learning architectures in comparison with SMFM, each box ...
Comparison of false positive ratio (FPR) and true positive ratio (TPR) for machine learning algorith...
Macro-F1 results comparison of seven widely used deep learning models under seven combinations of pr...
Macro-F1 results comparison of four widely used pre-training learning models under seven combination...
Macro-F1 comparisons of ten experiments with SVM, KNN, BPNN, CNN, ResNet, FA+ResNet models.</p
Comparison of machine learning model accuracy (ResNet101) with different combinations of analysis us...
<p>The left three models directly classify from text, the right two models are concept-extraction ba...
The mean result of machine learning models is determined by utilizing k-fold cross-validation. The a...
Comparing the performance of the GCNMLP with various machine learning methods for SIDER.</p
A count matrix undergoes pre-processing, including normalization and filtering. The data is randomly...
ROC curve comparing 6 machine learning algorithms for A) train and B) test data.</p
Comparison of out-of-sample results for stock chart images using the SC-CNN model.</p
<p>Comparison with PBF and DS preprocessing methods using different learning machine algorithms.</p
The evaluation results based on four simple machine learning models for two datasets.</p
<p>Three classifiers, Gaussian Naive Bayes (GNB) in panel (a), SVM in panel (b) and sparse MRF in pa...
(a) exhibits performance of different deep learning architectures in comparison with SMFM, each box ...
Comparison of false positive ratio (FPR) and true positive ratio (TPR) for machine learning algorith...
Macro-F1 results comparison of seven widely used deep learning models under seven combinations of pr...
Macro-F1 results comparison of four widely used pre-training learning models under seven combination...
Macro-F1 comparisons of ten experiments with SVM, KNN, BPNN, CNN, ResNet, FA+ResNet models.</p
Comparison of machine learning model accuracy (ResNet101) with different combinations of analysis us...
<p>The left three models directly classify from text, the right two models are concept-extraction ba...
The mean result of machine learning models is determined by utilizing k-fold cross-validation. The a...
Comparing the performance of the GCNMLP with various machine learning methods for SIDER.</p
A count matrix undergoes pre-processing, including normalization and filtering. The data is randomly...
ROC curve comparing 6 machine learning algorithms for A) train and B) test data.</p
Comparison of out-of-sample results for stock chart images using the SC-CNN model.</p
<p>Comparison with PBF and DS preprocessing methods using different learning machine algorithms.</p
The evaluation results based on four simple machine learning models for two datasets.</p
<p>Three classifiers, Gaussian Naive Bayes (GNB) in panel (a), SVM in panel (b) and sparse MRF in pa...
(a) exhibits performance of different deep learning architectures in comparison with SMFM, each box ...
Comparison of false positive ratio (FPR) and true positive ratio (TPR) for machine learning algorith...
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