The heatmaps show the accuracy of logistic regression models trained on one quantization gray-level and tested on all quantization gray-levels in the range 8–256 in steps of 8. The colormap is scaled to show a neutral gray for the accuracy obtained by assigning all predictions to the most common class, which is 0.5 for either the cerebellum or prefrontal cortex in Dataset 1 and 0.615 for benign glandular structures in Dataset 2. The upper row shows the result from Dataset 1 and the lower row shows the results from Dataset 2. The left column shows the original features and the right column shows the invariant features. The diagonal elements show the accuracies where the same quantization gray-levels were used for the training and test data.<...
(a) and (b) show the box plot of the five-class classification accuracy with RF and SVM, respectivel...
In this research the impact of different data representation on the performance of neural network an...
(a) The plots in the upper part depict examples of a binary classification task. The "x" and "o" sym...
To evaluate the performance of classifiers that were trained on a wide range of quantizations, 100 l...
CV thresholds of 2.5 were used for Multinomial Logistic Regression and Neural Networks while a thres...
<p>*Significant at p<0.05 utilizing 1000 permutation tests of sparse logistic regression SLR classif...
<p>Accuracy is shown for classifiers based on recursive feature elimination (solid blue line), rando...
Haralick texture features are common texture descriptors in image analysis. To compute the Haralick ...
Accuracy scores of multinomial logistic regression, SVM, and neural network with different number of...
<p>Lower and upper limits for the 95% bootstrap confidence intervals are also reported. 0.333 is the...
We modeled discrimination thresholds for object colors under different lighting environments [1]. Fi...
Learning probabilistic classification and prediction models that generate accurate probabilities is ...
Accurate calibration of probabilistic predictive models learned is critical for many practical predi...
<p>Data values are represented as grey level heat maps. MLR = multinomial logistic regression, MLP =...
Within each growth layout, a logistic regression was performed to classify connection existence from...
(a) and (b) show the box plot of the five-class classification accuracy with RF and SVM, respectivel...
In this research the impact of different data representation on the performance of neural network an...
(a) The plots in the upper part depict examples of a binary classification task. The "x" and "o" sym...
To evaluate the performance of classifiers that were trained on a wide range of quantizations, 100 l...
CV thresholds of 2.5 were used for Multinomial Logistic Regression and Neural Networks while a thres...
<p>*Significant at p<0.05 utilizing 1000 permutation tests of sparse logistic regression SLR classif...
<p>Accuracy is shown for classifiers based on recursive feature elimination (solid blue line), rando...
Haralick texture features are common texture descriptors in image analysis. To compute the Haralick ...
Accuracy scores of multinomial logistic regression, SVM, and neural network with different number of...
<p>Lower and upper limits for the 95% bootstrap confidence intervals are also reported. 0.333 is the...
We modeled discrimination thresholds for object colors under different lighting environments [1]. Fi...
Learning probabilistic classification and prediction models that generate accurate probabilities is ...
Accurate calibration of probabilistic predictive models learned is critical for many practical predi...
<p>Data values are represented as grey level heat maps. MLR = multinomial logistic regression, MLP =...
Within each growth layout, a logistic regression was performed to classify connection existence from...
(a) and (b) show the box plot of the five-class classification accuracy with RF and SVM, respectivel...
In this research the impact of different data representation on the performance of neural network an...
(a) The plots in the upper part depict examples of a binary classification task. The "x" and "o" sym...