Automated Machine Learning : Methods, Systems, Challenges
Mentés helye:
| Testületi szerző: | |
|---|---|
| Közreműködő(k): | |
| Különgyűjtemény: | e-book |
| Formátum: | könyv |
| Nyelv: | angol |
| Megjelenés: |
Cham : Springer International Publishing,
2019
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| Sorozat: | The Springer Series on Challenges in Machine Learning, ISSN 2520-131X |
| Tárgyszavak: | |
| Online elérés: | http://doi.org/10.1007/978-3-030-05318-5 |
| Címkék: |
Új címke
A tételhez itt fűzhet saját címkét!
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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), közreadó testület |
| author_corporate |
SpringerLink (Online service), közreadó testület |
| 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 |
2023-12-27T13:18:43Z |
| last_indexed |
2023-12-29T19:59:51Z |
| recordtype |
opac |
| publisher |
Cham : Springer International Publishing |
| _version_ |
1786643891513982976 |
| score |
13,3666115 |
| 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. |