A showcase of the use of autoencoders in feature learning applications

  1. David Charte
  2. Francisco Charte
  3. María del Jesus
  4. Francisco Herrera
Libro:
From Bioinspired Systems and Biomedical Applications to Machine Learning: 8th International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2019, Almería, Spain, June 3–7, 2019, Proceedings, Part II
  1. José Manuel Ferrández Vicente (dir. congr.)
  2. José Ramón Álvarez-Sánchez (dir. congr.)
  3. Félix de la Paz López (dir. congr.)
  4. Javier Toledo Moreo (dir. congr.)
  5. Hojjat Adeli (coord.)

Editorial: Springer Suiza

ISBN: 978-3-030-19651-6

Año de publicación: 2019

Páginas: 412-421

Tipo: Capítulo de Libro

DIALNET GOOGLE SCHOLAR

Resumen

Autoencoders are techniques for data representation learningbased on artificial neural networks. Differently to other feature learning methods which may be focused on finding specific transformations of the feature space, they can be adapted to fulfill many purposes, such as data visualization, denoising, anomaly detection and semantic hashing.This work presents these applications and provides details on howautoencoders can perform them, including code samples making use of an R package with an easy-to-use interface for autoencoder design and training, ruta. Along the way, the explanations on how each learning task has been achieved are provided with the aim to help the reader design their own autoencoders for these or other objectives.

Fuente de los datos: Dialnet