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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| Tác giả khác: | , , |
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| Định dạng: | Điện tử Chương của sách |
| Ngôn ngữ: | Tiếng Anh |
| Được phát hành: |
Cham
Springer Nature
2019
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| Loạt: | The Springer Series on Challenges in Machine Learning
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| Những chủ đề: | |
| Truy cập trực tuyến: | OAPEN Library: download the publication OAPEN Library: description of the publication |
| Các nhãn: |
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| Tóm tắt: | 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. |
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| Mô tả vật lý: | 1 electronic resource (219 p.) |
| số ISBN: | 978-3-030-05318-5 |
| Truy cập: | Open Access |