## 6 Recognition and Dataset
### Recognition Models  
Support vector machine (SVM) and random forest (RF) methods, as well as multilayer feedforward artificial neural network (ANN) models of machine learning, were used in the considered author recognition problem. ANNs used in the study differ from each other in terms of the number of layers, the number of neurons in these layers, and the values of synapses and biases among these neurons. In ANNs used for solving the considered author recognition problem, the number of neurons at the input layer--the first layer--is equal to the number of elements of the text feature vector, i.e., the number of elements in the feature group, and the number of neurons at the output layer--the last layer--is the number of authors. Intermediate layers, i.e., layers other than input or output layers, are called hidden layers. All ANNs used in the author recognition problem considered in the study are four-layered. In other words, the number of hidden layers is two. The number of neurons in the hidden layers of the ANNs used in the study differs according to the number of neurons in the input layer, i.e., the number of neurons in the last layer is the number of authors in the problem; in other words, the same for all ANNs used in the study. Let us note that the number of neurons in the input layer equals the number of features in the feature group. The number of neurons in the hidden layers in the ANNs used in the study varies from 3 to 10, and the total number of parameters--synapses and biases in the ANNs--varies from 271 to 325. In ANNs, SoftMax was used as the activation function for the last layer, and a rectified linear unit was used for the other layers. In the parametric identification of ANNs, the Adam optimization method [32] was used. This method should not be confused with the Adams method; the Adam method is a finite-dimensional unconstrained optimization method. For the stopping criterion, \"EarlyStopping\" was used [33]. This aims to stop optimization when the value of the object function does not improve. If the stopping condition \"EarlyStopping\" is not satisfied, an additional stopping condition is also used to stop the optimization; with this stopping condition, optimization stops when the number of epochs exceeds a sufficiently large number. By epoch, we mean at least one step using all the observations in the training set; for example, training 10 epochs means that each of the instances/observations in the training set was used 10 times in training. The upper limit for epochs was taken as 200 in the author recognition problem considered in the study. The radial basis function was used as a kernel in SVM. The number of trees--estimators used in RF--is 100, and the maximum depth of trees is 5. The 2-gram frequencies of the 100 most frequently used character n-grams in the texts in the training set were also used with a CNN [33] in the form of a 10 \(\times\) 10 matrix. The other machine learning methods and models used were tested with nearly all feature groups. The architecture of the used CNN consists of three convolution layers with 4, 4, 1 filters with dimensions of 3 \(\times\) 3, 3 \(\times\) 3, and 2 \(\times\) 2 with padding choices \"same\", \"valid\", or \"valid\", respectively, one subsampling layer after each of these convolution layers, and two fully connected layers with 4 and 11 neurons, respectively. For subsampling, layer average pooling was used. During parametric identification, the observations in the training set were involved in training in a mixed manner; in other words, observations in the training set were \"shuffled\" [33]. Since the number of observations belonging to different classes, i.e., authors in the training database differ from each other, the observations in the teaching set were given different class weights during their participation in training. The weight of each observation used in training is determined based on the number of observations in the class, i.e., the author of that observation.