Automated machine learning :  methods, systems, challenges / 
edited by Frank Hutter, Lars Kotthoff, and Joaquin Vanschoren 
 - Switzerland :  Springer,  c2019 
 - xiv, 219 pages :  illustrations ;  24 cm 
<br/><br/>Includes bibliographical references. 
<br/><br/>Part I : AutoML methods -- 1. Hyperparameter optimization -- 2. Meta-learning -- 3. Neural architecture search -- Part II : AutoML systems -- 4. Auto-WEKA : automatic model selection and hyperparameter optimization in WEKA -- 5. Hyperopt-sklearn -- 6. Auto-sklearn : efficient and robust automated machine learning -- 7. Towards automatically-tuned deep neural networks -- 8. TPOT : A Tree-based pipeline optimization tool for automating machine learning -- 9. The Automatic statistician -- Part II : AutoML challenges -- 10. Analysis of the autoML challenge series 2015-2018 
<br/><br/> This open access book presents the first comprehensive overview of general methods in autmated machine learning (AutoML), collects descriptions of existing systems based on these methiods, and discusses the first series of international challenges of AutoML systems. 
<br/><br/><label>ISBN: </label>9783030053178 
<br/><br/><label>Subjects--Topical Terms: </label><br/>ARTIFICIAL INTELLIGENCE
<br/><br/><label>LC Class. No.: </label>Q 325.5 .A98 2019