"Slicer: Feature Learning for Class Separability with Least-Squares Support ..."

David Charte et al. (2021)

> Home

Details and statistics

DOI: 10.1007/978-3-030-86271-8_26

access: closed

type: Conference or Workshop Paper

metadata version: 2021-09-23

- view
  - electronic edition via DOI
  - unpaywalled version
  authority control:
- export record
  dblp key:
  - conf/hais/CharteSLMCH21
- ask others
- share record
  persistent URL:
  - https://dblp.org/rec/conf/hais/CharteSLMCH21
David Charte, Iván Sevillano-García, María Jesús Lucena-González, José Luis Martín-Rodríguez, Francisco Charte, Francisco Herrera:
Slicer: Feature Learning for Class Separability with Least-Squares Support Vector Machine Loss and COVID-19 Chest X-Ray Case Study. HAIS 2021: 305-315

manage site settings

To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.