Automated Machine Learning: Methods, Systems, Challenges
Frank Hutter · Lars Kotthoff · Joaquin Vanschoren
ພ.ພ. 2019 · Springer
4,6star
14 ຄຳຕິຊົມreport
ປຶ້ມອີບຸກ
219
ໜ້າ
reportບໍ່ໄດ້ຢັ້ງຢືນການຈັດອັນດັບ ແລະ ຄຳຕິຊົມ ສຶກສາເພີ່ມເຕີມ
ກ່ຽວກັບປຶ້ມ e-book ນີ້
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.
ການຈັດອັນດັບ ແລະ ຄຳຕິຊົມ
4,6
14 ຄຳຕິຊົມ
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