Publication:
Automated Machine Learning

Simple item page
dc.contributor.author Frank Hutter, Lars Kotthoff, Joaquin Vanschoren (editors)
dc.date.accessioned 2023-06-19T01:34:12Z
dc.date.available 2023-06-19T01:34:12Z
dc.date.issued 2019
dc.description DOI: https://doi.org/10.1007/978-3-030-05318-5; License: CC BY; Publisher: Springer
dc.description.abstract 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.
dc.identifier.doi https://doi.org/10.1007/978-3-030-05318-5
dc.identifier.isbn 9783030053185
dc.identifier.uri http://repository.vlu.edu.vn:443/handle/123456789/5603
dc.language.iso en
dc.subject Machine learning
dc.subject Automated machine learning
dc.subject Automated data science
dc.subject Off-the-shelf machine learning
dc.subject Machine learning software
dc.subject Selecting a machine learning algorithm
dc.subject Tuning Hyperparameters
dc.subject Feature selection
dc.subject Preprocessing
dc.subject Deep learning
dc.subject Architecture search
dc.subject Machine learning pipeline optimization
dc.subject Open Access
dc.title Automated Machine Learning
dc.type Resource Types::text::book
dspace.entity.type Publication
oairecerif.author.affiliation #PLACEHOLDER_PARENT_METADATA_VALUE#
Files
Original bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
OAB345.txt
Size:
0 B
Format:
Plain Text
Description:
Download
Collections
Open Access Books - Technology - 2019