Subject-wise two-class classification of positive and negative displays based on best feature combination (%).</p
<p>Each algorithm trained using selected features and evaluated with 10-fold cross-validation. Value...
<p>Distribution of images across the valence range in each class of unpleasant, neutral, and pleasan...
Comparison of accuracy of best machine-learners on various feature categories.</p
Subject-wise four-class classification of positive vs. negative displays based on best feature combi...
Feature-wise four-class classification of positive and negative displays averaged over 20 subjects (...
Feature-wise two-class classification of positive and negative displays averaged over 20 subjects (%...
<p>Combinations indicated by green lines are used for extracting positive shape-indexed features, an...
<p>Classification accuracies(%) of the subjects under the two different conditions.</p
The averaged maps (over 20 subjects) for 4 categories of positive and negative displays.</p
Comparison of classification accuracies of dataset 2 with different classifiers.</p
Comparison of the classification accuracies of different algorithms and different feature fusion met...
<p>Comparison of the classification results of original and selected features.</p
<p>Comparison of average classification results of different classifiers without using light intensi...
<p>PCHIP and moving average features classify better than conventional parameters, and slightly bett...
Comparison of classification accuracies of dataset 1 with different classifiers.</p
<p>Each algorithm trained using selected features and evaluated with 10-fold cross-validation. Value...
<p>Distribution of images across the valence range in each class of unpleasant, neutral, and pleasan...
Comparison of accuracy of best machine-learners on various feature categories.</p
Subject-wise four-class classification of positive vs. negative displays based on best feature combi...
Feature-wise four-class classification of positive and negative displays averaged over 20 subjects (...
Feature-wise two-class classification of positive and negative displays averaged over 20 subjects (%...
<p>Combinations indicated by green lines are used for extracting positive shape-indexed features, an...
<p>Classification accuracies(%) of the subjects under the two different conditions.</p
The averaged maps (over 20 subjects) for 4 categories of positive and negative displays.</p
Comparison of classification accuracies of dataset 2 with different classifiers.</p
Comparison of the classification accuracies of different algorithms and different feature fusion met...
<p>Comparison of the classification results of original and selected features.</p
<p>Comparison of average classification results of different classifiers without using light intensi...
<p>PCHIP and moving average features classify better than conventional parameters, and slightly bett...
Comparison of classification accuracies of dataset 1 with different classifiers.</p
<p>Each algorithm trained using selected features and evaluated with 10-fold cross-validation. Value...
<p>Distribution of images across the valence range in each class of unpleasant, neutral, and pleasan...
Comparison of accuracy of best machine-learners on various feature categories.</p
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