<p>Top row: The Expert Labeled dataset was used a gold standard to analyze how well the different experimental groups (blue boxes) performed. Bottom row: the labeling from each experimental group was used to train an ML classifier. Each ML classifier was then tested against an expert-labeled test set.</p
<p>(A) Training set marker states. The eight possible marker states for the three indicated markers ...
Machine learning (ML) over tabular data has become ubiquitous with applications in many domains. Thi...
(A) Top: Schema of learning curve analysis in sessions: average of blocks of 4 visits starting after...
<p>Colored boxes (gray/green) depict different training data sets. Step 1- assessment of individual ...
<p>(a) Training samples = 60, testing samples = 210, number of features = 4. (b) Training samples = ...
<p>(The feature sets are labeled by their size, here 6 features, and then enumerated from 0 to N–1, ...
A count matrix undergoes pre-processing, including normalization and filtering. The data is randomly...
<p>(A) In the within-dataset experiments, part of the training set, referred as the marker-evaluatio...
The process of FS and classification consists of the following steps: 1) create 100 random splits of...
Cross-validated performance is shown for the Super Learner and the top three individual models for (...
<p>Accuracies for the 12 participants for each type of classification are summarized in a boxplot. O...
<p>Plot shows the pairwise differences in performance among classifiers. The horizontal scale shows ...
<p>The second column gives the performance of an Elastic Net model under cross-validation on the tra...
(A) Visualization of the entire classification training process. After ground truth data were select...
(a) exhibits performance of different deep learning architectures in comparison with SMFM, each box ...
<p>(A) Training set marker states. The eight possible marker states for the three indicated markers ...
Machine learning (ML) over tabular data has become ubiquitous with applications in many domains. Thi...
(A) Top: Schema of learning curve analysis in sessions: average of blocks of 4 visits starting after...
<p>Colored boxes (gray/green) depict different training data sets. Step 1- assessment of individual ...
<p>(a) Training samples = 60, testing samples = 210, number of features = 4. (b) Training samples = ...
<p>(The feature sets are labeled by their size, here 6 features, and then enumerated from 0 to N–1, ...
A count matrix undergoes pre-processing, including normalization and filtering. The data is randomly...
<p>(A) In the within-dataset experiments, part of the training set, referred as the marker-evaluatio...
The process of FS and classification consists of the following steps: 1) create 100 random splits of...
Cross-validated performance is shown for the Super Learner and the top three individual models for (...
<p>Accuracies for the 12 participants for each type of classification are summarized in a boxplot. O...
<p>Plot shows the pairwise differences in performance among classifiers. The horizontal scale shows ...
<p>The second column gives the performance of an Elastic Net model under cross-validation on the tra...
(A) Visualization of the entire classification training process. After ground truth data were select...
(a) exhibits performance of different deep learning architectures in comparison with SMFM, each box ...
<p>(A) Training set marker states. The eight possible marker states for the three indicated markers ...
Machine learning (ML) over tabular data has become ubiquitous with applications in many domains. Thi...
(A) Top: Schema of learning curve analysis in sessions: average of blocks of 4 visits starting after...