The area of each square represents the value of each matrix entry. Values are counts averaged across our subjectwise 10-fold cross-validation. The intervals (±) associated with each value show s.t.d. across folds. Overall, the network performed with accuracy = 76.8%, as indicated by the mass along the matrix’s main diagonal.</p
Results are averaged across observers and execution of transformation and viewpoint. Actual transfor...
The colours of the heat map correspond to the percentage of classification in each category. The acc...
Mean error = 0.64% (CI = 0.54–0.74%) estimated from RF training repetitions. Green circles represent...
<p>(A), Confusion matrix of the model performance on the dataset of 15 scenes. The average accuracy ...
<p>The upper number in each entry of the matrix is the average number of actual recognised classes i...
The confusion matrix representing the computed classification accuracy % for the proposed research w...
The correctly classified data is reflected along the diagonal regions. The misclassified is reflecte...
The confusion matrix representing the computed classification accuracy % for the proposed research w...
<p>The rows of this matrix indicate the groups of the subjects (ground truth), and the columns indic...
<p>The subfigures correspond to the performance of the algorithm using different numbers of labeled ...
<p>Sizes S1, S2, S3 and S4 with non-rotated ligatures are shown along y-axis, as a test data. The co...
Classifications of the validation data (columns) are compared to the ground truth (rows). Numbers ar...
Confusion matrix showing the results of the testing phase for the AlexNet neural network.</p
Confusion matrix for the best classification model, which corresponded to a neural network with four...
Actual materials on the y axis are plotted against perceived materials on the x axis. R2 values indi...
Results are averaged across observers and execution of transformation and viewpoint. Actual transfor...
The colours of the heat map correspond to the percentage of classification in each category. The acc...
Mean error = 0.64% (CI = 0.54–0.74%) estimated from RF training repetitions. Green circles represent...
<p>(A), Confusion matrix of the model performance on the dataset of 15 scenes. The average accuracy ...
<p>The upper number in each entry of the matrix is the average number of actual recognised classes i...
The confusion matrix representing the computed classification accuracy % for the proposed research w...
The correctly classified data is reflected along the diagonal regions. The misclassified is reflecte...
The confusion matrix representing the computed classification accuracy % for the proposed research w...
<p>The rows of this matrix indicate the groups of the subjects (ground truth), and the columns indic...
<p>The subfigures correspond to the performance of the algorithm using different numbers of labeled ...
<p>Sizes S1, S2, S3 and S4 with non-rotated ligatures are shown along y-axis, as a test data. The co...
Classifications of the validation data (columns) are compared to the ground truth (rows). Numbers ar...
Confusion matrix showing the results of the testing phase for the AlexNet neural network.</p
Confusion matrix for the best classification model, which corresponded to a neural network with four...
Actual materials on the y axis are plotted against perceived materials on the x axis. R2 values indi...
Results are averaged across observers and execution of transformation and viewpoint. Actual transfor...
The colours of the heat map correspond to the percentage of classification in each category. The acc...
Mean error = 0.64% (CI = 0.54–0.74%) estimated from RF training repetitions. Green circles represent...