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• Title:Automated Machine Learning [electronic resource] : Methods, Systems, Challenges / edited by Frank Hutter, Lars Kotthoff, Joaquin Vanschoren.
  
• ISBN:9783030053185
  
• Publication:Cham : Springer International Publishing : Imprint: Springer, 2019.
  
• Physical Description:XIV, 219 p. 54 illus., 45 illus. in color.
  
• Links:Online book
  

• Yale Holdings

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  • Location:Yale Internet Resource
    Information about Off Campus Access
    
    
  • Call Number: None
    
  • Status:No information available 
  • Notes: Online resource
    

   
• Local Notes:Access is available to the Yale community.
  
• Access and use:Access restricted by licensing agreement.
• Summary: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 ML applications and the rapid growth of the field has created a high demand for off-the-shelf ML methods that can be used easily and without expert knowledge. However, many of the recent machine learning successes crucially rely on human experts, who manually select appropriate ML architectures (deep learning architectures or more traditional ML workflows) and their hyperparameters. To overcome this problem, the field of AutoML targets a progressive automation of machine learning, based on principles from optimization and machine learning itself. This book serves as a point of entry into this quickly-developing field for researchers and advanced students alike, as well as providing a reference for practitioners aiming to use AutoML in their work.
  
• In_:Springer eBooks
  
• Variant and related titles:Springer ENIN.
  
• Other formats:Printed edition:
  Printed edition:
  
• Format:Book
• Series:The Springer Series on Challenges in Machine Learning,
  Springer series on challenges in machine learning.
  
• 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.
  
• Subjects:Artificial intelligence.
  Computer vision.
  Optical pattern recognition.
  
• Also listed under:Hutter, Frank.
  Kotthoff, Lars.
  Vanschoren, Joaquin.
  SpringerLink (Online service)