Generative methods in classification tasks

Abstract

The paper presents the implementation of generative methods in classification tasks. A distinction is made between
two types of tasks – supervised learning and unsupervised learning – along with example use cases. Within the scope of
supervised methods, described are the Bayes classifier and the use of the multivariate Gaussian distribution. To solve
the unsupervised learning task using a generative approach, the Gaussian Mixture Model (GMM) is presented. The
paper also describes a generative neural network based on an autoencoder architecture, implemented as a Variational
Autoencoder (VAE).