Automated Machine Learning : Methods, Systems, Challenges

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Special Collection:e-book
Format: Book
Language:English
Published: Cham : Springer International Publishing, 2019
Series:The Springer Series on Challenges in Machine Learning, ISSN 2520-131X
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Online Access:http://doi.org/10.1007/978-3-030-05318-5
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id opac-EUL01-001012984
collection e-book
institution L_042
EUL01
spelling Automated Machine Learning Methods, Systems, Challenges edited by Frank Hutter, Lars Kotthoff, Joaquin Vanschoren.
Cham Springer International Publishing 2019
XIV, 219 p. 54 illusztrált, 45 illusztrált szinesben online forrás
szöveg txt rdacontent
számítógépes c rdamedia
távoli hozzáférés cr rdacarrier
szövegfájl PDF rda
The Springer Series on Challenges in Machine Learning 2520-131X
1 Hyperparameter Optimization -- 2 Meta-Learning -- 3 Neural Architecture Search -- 4 Auto-WEKA -- 5 Hyperopt-Sklearn -- 6 Auto-sklearn -- 7 Towards Automatically-Tuned Deep Neural Networks -- 8 TPOT -- 9 The Automatic Statistician -- 10 AutoML 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 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.
Nyomtatott kiadás: ISBN 9783030053178
Nyomtatott kiadás: ISBN 9783030053192
Az e-könyvek a teljes ELTE IP-tartományon belül online elérhetők.
e-book
könyv
optikai adatfeldolgozás EUL10000808473 Y
alakfelismerés EUL10000324145 Y
Artificial intelligence. EUL10000183324 Y
Optical data processing.
Pattern recognition. EUL10000225246 Y
elektronikus könyv
Hutter, Frank szerk. EUL10001080417 Y
Kotthoff, Lars szerk. EUL10001080418 Y
Vanschoren, Joaquin szerk. EUL10001080420 Y
SpringerLink (Online service) közreadó testület
The Springer Series on Challenges in Machine Learning EUL10001010598 Y
Online változat http://doi.org/10.1007/978-3-030-05318-5
Cham Springer International Publishing Imprint: Springer, 2019
EUL01
language English
format Book
author2 Hutter, Frank, szerk.
Kotthoff, Lars, szerk.
Vanschoren, Joaquin, szerk.
author_facet Hutter, Frank, szerk.
Kotthoff, Lars, szerk.
Vanschoren, Joaquin, szerk.
SpringerLink (Online service)
author_corporate SpringerLink (Online service)
author_sort Hutter, Frank
title Automated Machine Learning : Methods, Systems, Challenges
spellingShingle Automated Machine Learning : Methods, Systems, Challenges
The Springer Series on Challenges in Machine Learning, ISSN 2520-131X
optikai adatfeldolgozás
alakfelismerés
Artificial intelligence.
Optical data processing.
Pattern recognition.
elektronikus könyv
title_sub Methods, Systems, Challenges
title_short Automated Machine Learning
title_full Automated Machine Learning Methods, Systems, Challenges edited by Frank Hutter, Lars Kotthoff, Joaquin Vanschoren.
title_fullStr Automated Machine Learning Methods, Systems, Challenges edited by Frank Hutter, Lars Kotthoff, Joaquin Vanschoren.
title_full_unstemmed Automated Machine Learning Methods, Systems, Challenges edited by Frank Hutter, Lars Kotthoff, Joaquin Vanschoren.
title_auth Automated Machine Learning Methods, Systems, Challenges
title_sort automated machine learning methods systems challenges
series The Springer Series on Challenges in Machine Learning, ISSN 2520-131X
series2 The Springer Series on Challenges in Machine Learning
publishDate 2019
publishDateSort 2019
physical XIV, 219 p. 54 illusztrált, 45 illusztrált szinesben : online forrás
isbn 978-3-030-05318-5
issn 2520-131X
callnumber-first Q - Science
callnumber-subject Q - General Science
callnumber-label Q334-342
callnumber-raw 15551
callnumber-search 15551
topic optikai adatfeldolgozás
alakfelismerés
Artificial intelligence.
Optical data processing.
Pattern recognition.
elektronikus könyv
topic_facet optikai adatfeldolgozás
alakfelismerés
Artificial intelligence.
Optical data processing.
Pattern recognition.
elektronikus könyv
optikai adatfeldolgozás
alakfelismerés
Artificial intelligence.
Optical data processing.
Pattern recognition.
url http://doi.org/10.1007/978-3-030-05318-5
illustrated Illustrated
dewey-hundreds 000 - Computer science, information & general works
dewey-tens 000 - Computer science, knowledge & systems
dewey-ones 006 - Special computer methods
dewey-full 006.3
dewey-sort 16.3
dewey-raw 006.3
dewey-search 006.3
first_indexed 2021-04-13T13:24:28Z
last_indexed 2021-04-14T06:48:12Z
recordtype opac
publisher Cham : Springer International Publishing
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score 13,340887
generalnotes 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.