Automated Machine Learning : Methods, Systems, Challenges

Automated Machine Learning Methods, Systems, Challenges

This open access book presents the first comprehensive overview of general methods in Automated Machine Learning (AutoML), collects descriptions of existing systems based on these methods, and discusses the first series of international challenges of AutoML systems. The recent success of commercial...

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Bibliographic Details
Other Authors: Hutter, Frank (Editor), Hutter, Frank. editor (editor), Kotthoff, Lars. editor, Vanschoren, Joaquin. editor
Format: eBook
Language:Inglés
Published: Cham Springer Nature 2019
Cham : 2019.
Edition:1st ed. 2019.
Series:The Springer Series on Challenges in Machine Learning,
Subjects:
Artificial intelligence.
Optical data processing.
Pattern recognition.
Artificial Intelligence.
Image Processing and Computer Vision.
Pattern Recognition.
Computer science
Artificial intelligence
Optical data processing
Pattern recognition
See on Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009430449206719
Table of Contents:
  • 1 Hyperparameter Optimization
  • 2 Meta-Learning
  • 3 Neural Architecture Search
  • 4 Auto-WEKA
  • 5 Hyperopt-Sklearn
  • 6 Auto-sklearn
  • 7 Towards Automatically-Tuned Deep Neural Networks
  • 8 TPOT
  • 9 The Automatic Statistician
  • 10 AutoML Challenges.

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