Which of the following is NOT an example of a generative model?

Support Vector Machines (SVMs) are primarily designed for classification and regression tasks by finding the optimal hyperplane that separates different classes in a dataset. They do not model the distribution of the data; rather, they focus on the decision boundary between classes. Generative models, on the other hand, are designed to model how data is generated, capturing the underlying distribution from which the data originates.

In contrast, Markov Chains, Generative Adversarial Networks (GANs), and Variational Autoencoders (VAEs) are all examples of generative models. Markov Chains can generate sequences of data based on transition probabilities between states. GANs consist of two neural networks that work against each other to generate new data instances that resemble the training data. VAEs use encoding and decoding mechanisms to generate new data by sampling from a learned distribution.

Thus, designating SVMs as not being a generative model accurately reflects their function, which is fundamentally distinct from that of models that generate data based on probabilistic distributions.