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Fundamentals of Machine Learning$
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Thomas P. Trappenberg

Print publication date: 2019

Print ISBN-13: 9780198828044

Published to Oxford Scholarship Online: January 2020

DOI: 10.1093/oso/9780198828044.001.0001

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Generative models

Generative models

Chapter:
(p.162) 8 Generative models
Source:
Fundamentals of Machine Learning
Author(s):

Thomas P. Trappenberg

Publisher:
Oxford University Press
DOI:10.1093/oso/9780198828044.003.0008

This chapter presents an introduction to the important topic of building generative models. These are models that are aimed to understand the variety of a class such as cars or trees. A generative mode should be able to generate feature vectors for instances of the class they represent, and such models should therefore be able to characterize the class with all its variations. The subject is discussed both in a Bayesian and in a deep learning context, and also within a supervised and unsupervised context. This area is related to important algorithms such as k-means clustering, expectation maximization (EM), naïve Bayes, generative adversarial networks (GANs), and variational autoencoders (VAE).

Keywords:   generative models, naïve Bayes, k-means, expectation maximization, generative adversarial networks (GANs), variational autoencoder (VAE)

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