TY  - BOOK
AU  - Hutter,Frank
AU  - Kotthoff,Lars
AU  - Vanschoren,Joaquin
ED  - SpringerLink (Online service)
TI  - Automated Machine Learning: Methods, Systems, Challenges 
T2  - The Springer Series on Challenges in Machine Learning,
SN  - 9783030053185
AV  - Q334-342 
U1  - 006.3 23
PY  - 2019///
CY  - Cham
PB  - Springer International Publishing, Imprint: Springer
KW  - Artificial intelligence
KW  - Optical data processing
KW  - Pattern recognition
KW  - Artificial Intelligence
KW  - Image Processing and Computer Vision
KW  - Pattern Recognition
N1  - 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; Open Access
N2  - 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
UR  - https://doi.org/10.1007/978-3-030-05318-5
ER  -