For each model and each size Ntr of the training data, we sampled 150 training data sets with input dimension d ∈ {8, 9, 10, 11, 12} and visualized the measured performance as box plots.</p
(a) The plots in the upper part depict examples of a binary classification task. The "x" and "o" sym...
Abstract: In numerous binary classification tasks, the two groups of instances are not equally repre...
A) A typical distribution of states in a sample training set (N = 9009). B) A visualization of the p...
Abstract. We propose a novel approach for the estimation of the size of training sets that are neede...
<p>The box plots show the distribution of values, normalized to range between 0 and 1, by feature an...
In this chapter, we present a simple classification scheme that utilizes only 1-bit measurements of ...
<p>(a) Training samples = 60, testing samples = 210, number of features = 4. (b) Training samples = ...
<p>This figure exemplifies a classification analysis, which is used to infer on the link between a c...
<p>Accuracies are mean accuracies of test set performance over ten folds. (* 0.001</p
Overview of the five real-world data sets and their basic characteristics: n is the number of classe...
An exemplary binary classification problem with one-dimensional feature space and class labels y ∈ ...
(a) and (b) show the box plot of the five-class classification accuracy with RF and SVM, respectivel...
<p>The left chart is for binary and the right for multi-class classification. The X axis in both ref...
<p>Data for the three simulation analyses were generated by adjusting three factors (sample size, nu...
Comparison of using different data compositions of synthetic images for training the classifier and ...
(a) The plots in the upper part depict examples of a binary classification task. The "x" and "o" sym...
Abstract: In numerous binary classification tasks, the two groups of instances are not equally repre...
A) A typical distribution of states in a sample training set (N = 9009). B) A visualization of the p...
Abstract. We propose a novel approach for the estimation of the size of training sets that are neede...
<p>The box plots show the distribution of values, normalized to range between 0 and 1, by feature an...
In this chapter, we present a simple classification scheme that utilizes only 1-bit measurements of ...
<p>(a) Training samples = 60, testing samples = 210, number of features = 4. (b) Training samples = ...
<p>This figure exemplifies a classification analysis, which is used to infer on the link between a c...
<p>Accuracies are mean accuracies of test set performance over ten folds. (* 0.001</p
Overview of the five real-world data sets and their basic characteristics: n is the number of classe...
An exemplary binary classification problem with one-dimensional feature space and class labels y ∈ ...
(a) and (b) show the box plot of the five-class classification accuracy with RF and SVM, respectivel...
<p>The left chart is for binary and the right for multi-class classification. The X axis in both ref...
<p>Data for the three simulation analyses were generated by adjusting three factors (sample size, nu...
Comparison of using different data compositions of synthetic images for training the classifier and ...
(a) The plots in the upper part depict examples of a binary classification task. The "x" and "o" sym...
Abstract: In numerous binary classification tasks, the two groups of instances are not equally repre...
A) A typical distribution of states in a sample training set (N = 9009). B) A visualization of the p...