Automated Machine Learning Methods, Systems, Challenges /

• निगमित लेखक: SpringerLink (Online service)
• अन्य लेखक: Hutter, Frank (संपादक), Kotthoff, Lars (संपादक), Vanschoren, Joaquin (संपादक)
• स्वरूप: इलेक्ट्रोनिक ई-पुस्तक
• भाषा: अंग्रेज़ी
• प्रकाशित: Cham : Springer International Publishing : Imprint: Springer, 2019.
• संस्करण: 1st ed. 2019.
• श्रृंखला: The Springer Series on Challenges in Machine Learning,
• विषय: Artificial intelligence.; Computer vision.; Pattern recognition systems.; Artificial Intelligence.; Computer Vision.; Automated Pattern Recognition.
• ऑनलाइन पहुंच: Link to Metadata

सारांश

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.

भौतिक वर्णन

XIV, 219 p. 54 illus., 45 illus. in color. online resource.

आईएसबीएन

9783030053185

आईएसएसएन

2520-1328

डिजिटल ऑब्जेक्ट पहचानकर्ता

10.1007/978-3-030-05318-5

अभिगमन

Open Access