Methods, Systems, Challenges Frank Hutter Primary Author Lars Kotthoff Primary Author Joaquin Vanschoren Primary Author mixed material bibliography Cham Springer Nature 2019 en English

Ebook

219 Halaman 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. Pembelajaran Mesin Kecerdasan Buatan 006.31 9783030053185 Perpustakaan UMA Online Public Access Catalog (OPAC) 006.31 Fra a EB26000244 Perpustakaan Pusat UMA 006.31 Fra a Automated Machine Learning : Methods, Systems, Challenges 1007149.pdf.jpg.jpg 23700 2026-09-04 13:44:06 2026-10-12 15:07:12 machine generated