4 options
Automated Machine Learning : Methods, Systems, Challenges / edited by Frank Hutter, Lars Kotthoff, Joaquin Vanschoren.
Springer Nature - Springer Nature Link Journals and eBooks - Fully Open Access Available online
View online- Format:
- Book
- Author/Creator:
- Hutter, Frank., Editor.
- Series:
- The Springer Series on Challenges in Machine Learning, 2520-131X
- Language:
- English
- Subjects (All):
- Artificial intelligence.
- Optical data processing.
- Pattern perception.
- Artificial Intelligence.
- Image Processing and Computer Vision.
- Pattern Recognition.
- Local Subjects:
- Artificial Intelligence.
- Image Processing and Computer Vision.
- Pattern Recognition.
- Physical Description:
- 1 online resource (XIV, 219 p. 54 illus., 45 illus. in color.)
- Edition:
- 1st ed. 2019.
- Place of Publication:
- Cham Springer Nature 2019
- Cham : Springer International Publishing : Imprint: Springer, 2019.
- Language Note:
- English
- 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.
- 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.
- Notes:
- Description based on publisher supplied metadata and other sources.
- ISBN:
- 3-030-05318-0
- OCLC:
- 1105039769
- Access Restriction:
- Open access Unrestricted online access
The Penn Libraries is committed to describing library materials using current, accurate, and responsible language. If you discover outdated or inaccurate language, please fill out this feedback form to report it and suggest alternative language.