Automated Machine Learning : (Record no. 305263)

MARC details
000 -LEADER
fixed length control field 07368nam a22004573i 4500
001 - CONTROL NUMBER
control field EBC5788944
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20240122001149.0
006 - FIXED-LENGTH DATA ELEMENTS--ADDITIONAL MATERIAL CHARACTERISTICS
fixed length control field m o d |
007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION
fixed length control field cr cnu||||||||
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 231124s2019 xx o ||||0 eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9783030053185
Qualifying information (electronic bk.)
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
Canceled/invalid ISBN 9783030053178
035 ## - SYSTEM CONTROL NUMBER
System control number (MiAaPQ)EBC5788944
035 ## - SYSTEM CONTROL NUMBER
System control number (Au-PeEL)EBL5788944
035 ## - SYSTEM CONTROL NUMBER
System control number (OCoLC)1105039769
040 ## - CATALOGING SOURCE
Original cataloging agency MiAaPQ
Language of cataloging eng
Description conventions rda
-- pn
Transcribing agency MiAaPQ
Modifying agency MiAaPQ
050 #4 - LIBRARY OF CONGRESS CALL NUMBER
Classification number Q334-342
100 1# - MAIN ENTRY--PERSONAL NAME
Personal name Hutter, Frank.
245 10 - TITLE STATEMENT
Title Automated Machine Learning :
Remainder of title Methods, Systems, Challenges.
250 ## - EDITION STATEMENT
Edition statement 1st ed.
264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE
Place of production, publication, distribution, manufacture Cham :
Name of producer, publisher, distributor, manufacturer Springer International Publishing AG,
Date of production, publication, distribution, manufacture, or copyright notice 2019.
264 #4 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE
Date of production, publication, distribution, manufacture, or copyright notice �2019.
300 ## - PHYSICAL DESCRIPTION
Extent 1 online resource (223 pages)
336 ## - CONTENT TYPE
Content type term text
Content type code txt
Source rdacontent
337 ## - MEDIA TYPE
Media type term computer
Media type code c
Source rdamedia
338 ## - CARRIER TYPE
Carrier type term online resource
Carrier type code cr
Source rdacarrier
490 1# - SERIES STATEMENT
Series statement The Springer Series on Challenges in Machine Learning Series
505 0# - FORMATTED CONTENTS NOTE
Formatted contents note Intro -- Foreword -- Preface -- Acknowledgments -- Contents -- Part I AutoML Methods -- 1 Hyperparameter Optimization -- 1.1 Introduction -- 1.2 Problem Statement -- 1.2.1 Alternatives to Optimization: Ensembling and Marginalization -- 1.2.2 Optimizing for Multiple Objectives -- 1.3 Blackbox Hyperparameter Optimization -- 1.3.1 Model-Free Blackbox Optimization Methods -- 1.3.2 Bayesian Optimization -- 1.3.2.1 Bayesian Optimization in a Nutshell -- 1.3.2.2 Surrogate Models -- 1.3.2.3 Configuration Space Description -- 1.3.2.4 Constrained Bayesian Optimization -- 1.4 Multi-fidelity Optimization -- 1.4.1 Learning Curve-Based Prediction for Early Stopping -- 1.4.2 Bandit-Based Algorithm Selection Methods -- 1.4.3 Adaptive Choices of Fidelities -- 1.5 Applications to AutoML -- 1.6 Open Problems and Future Research Directions -- 1.6.1 Benchmarks and Comparability -- 1.6.2 Gradient-Based Optimization -- 1.6.3 Scalability -- 1.6.4 Overfitting and Generalization -- 1.6.5 Arbitrary-Size Pipeline Construction -- Bibliography -- 2 Meta-Learning -- 2.1 Introduction -- 2.2 Learning from Model Evaluations -- 2.2.1 Task-Independent Recommendations -- 2.2.2 Configuration Space Design -- 2.2.3 Configuration Transfer -- 2.2.3.1 Relative Landmarks -- 2.2.3.2 Surrogate Models -- 2.2.3.3 Warm-Started Multi-task Learning -- 2.2.3.4 Other Techniques -- 2.2.4 Learning Curves -- 2.3 Learning from Task Properties -- 2.3.1 Meta-Features -- 2.3.2 Learning Meta-Features -- 2.3.3 Warm-Starting Optimization from Similar Tasks -- 2.3.4 Meta-Models -- 2.3.4.1 Ranking -- 2.3.4.2 Performance Prediction -- 2.3.5 Pipeline Synthesis -- 2.3.6 To Tune or Not to Tune? -- 2.4 Learning from Prior Models -- 2.4.1 Transfer Learning -- 2.4.2 Meta-Learning in Neural Networks -- 2.4.3 Few-Shot Learning -- 2.4.4 Beyond Supervised Learning -- 2.5 Conclusion -- Bibliography.
505 8# - FORMATTED CONTENTS NOTE
Formatted contents note 3 Neural Architecture Search -- 3.1 Introduction -- 3.2 Search Space -- 3.3 Search Strategy -- 3.4 Performance Estimation Strategy -- 3.5 Future Directions -- Bibliography -- Part II AutoML Systems -- 4 Auto-WEKA: Automatic Model Selection and Hyperparameter Optimization in WEKA -- 4.1 Introduction -- 4.2 Preliminaries -- 4.2.1 Model Selection -- 4.2.2 Hyperparameter Optimization -- 4.3 CASH -- 4.3.1 Sequential Model-Based Algorithm Configuration (SMAC) -- 4.4 Auto-WEKA -- 4.5 Experimental Evaluation -- 4.5.1 Baseline Methods -- 4.5.2 Results for Cross-Validation Performance -- 4.5.3 Results for Test Performance -- 4.6 Conclusion -- 4.6.1 Community Adoption -- Bibliography -- 5 Hyperopt-Sklearn -- 5.1 Introduction -- 5.2 Background: Hyperopt for Optimization -- 5.3 Scikit-Learn Model Selection as a Search Problem -- 5.4 Example Usage -- 5.5 Experiments -- 5.6 Discussion and Future Work -- 5.7 Conclusions -- Bibliography -- 6 Auto-sklearn: Efficient and Robust Automated MachineLearning -- 6.1 Introduction -- 6.2 AutoML as a CASH Problem -- 6.3 New Methods for Increasing Efficiency and Robustness of AutoML -- 6.3.1 Meta-learning for Finding Good Instantiations of Machine Learning Frameworks -- 6.3.2 Automated Ensemble Construction of Models Evaluated During Optimization -- 6.4 A Practical Automated Machine Learning System -- 6.5 Comparing Auto-sklearn to Auto-WEKA and Hyperopt-Sklearn -- 6.6 Evaluation of the Proposed AutoML Improvements -- 6.7 Detailed Analysis of Auto-sklearn Components -- 6.8 Discussion and Conclusion -- 6.8.1 Discussion -- 6.8.2 Usage -- 6.8.3 Extensions in PoSH Auto-sklearn -- 6.8.4 Conclusion and Future Work -- Bibliography -- 7 Towards Automatically-Tuned Deep Neural Networks -- 7.1 Introduction -- 7.2 Auto-Net 1.0 -- 7.3 Auto-Net 2.0 -- 7.4 Experiments -- 7.4.1 Baseline Evaluation of Auto-Net 1.0 and Auto-sklearn.
505 8# - FORMATTED CONTENTS NOTE
Formatted contents note 7.4.2 Results for AutoML Competition Datasets -- 7.4.3 Comparing AutoNet 1.0 and 2.0 -- 7.5 Conclusion -- Bibliography -- 8 TPOT: A Tree-Based Pipeline Optimization Toolfor Automating Machine Learning -- 8.1 Introduction -- 8.2 Methods -- 8.2.1 Machine Learning Pipeline Operators -- 8.2.2 Constructing Tree-Based Pipelines -- 8.2.3 Optimizing Tree-Based Pipelines -- 8.2.4 Benchmark Data -- 8.3 Results -- 8.4 Conclusions and Future Work -- Bibliography -- 9 The Automatic Statistician -- 9.1 Introduction -- 9.2 Basic Anatomy of an Automatic Statistician -- 9.2.1 Related Work -- 9.3 An Automatic Statistician for Time Series Data -- 9.3.1 The Grammar over Kernels -- 9.3.2 The Search and Evaluation Procedure -- 9.3.3 Generating Descriptions in Natural Language -- 9.3.4 Comparison with Humans -- 9.4 Other Automatic Statistician Systems -- 9.4.1 Core Components -- 9.4.2 Design Challenges -- 9.4.2.1 User Interaction -- 9.4.2.2 Missing and Messy Data -- 9.4.2.3 Resource Allocation -- 9.5 Conclusion -- Bibliography -- Part III AutoML Challenges -- 10 Analysis of the AutoML Challenge Series 2015-2018 -- 10.1 Introduction -- 10.2 Problem Formalization and Overview -- 10.2.1 Scope of the Problem -- 10.2.2 Full Model Selection -- 10.2.3 Optimization of Hyper-parameters -- 10.2.4 Strategies of Model Search -- 10.3 Data -- 10.4 Challenge Protocol -- 10.4.1 Time Budget and Computational Resources -- 10.4.2 Scoring Metrics -- 10.4.3 Rounds and Phases in the 2015/2016 Challenge -- 10.4.4 Phases in the 2018 Challenge -- 10.5 Results -- 10.5.1 Scores Obtained in the 2015/2016 Challenge -- 10.5.2 Scores Obtained in the 2018 Challenge -- 10.5.3 Difficulty of Datasets/Tasks -- 10.5.4 Hyper-parameter Optimization -- 10.5.5 Meta-learning -- 10.5.6 Methods Used in the Challenges -- 10.6 Discussion -- 10.7 Conclusion -- Bibliography -- Correction to: Neural Architecture Search.
588 ## - SOURCE OF DESCRIPTION NOTE
Source of description note Description based on publisher supplied metadata and other sources.
590 ## - LOCAL NOTE (RLIN)
Local note Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2023. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.
655 #4 - INDEX TERM--GENRE/FORM
Genre/form data or focus term Electronic books.
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Kotthoff, Lars.
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Vanschoren, Joaquin.
776 08 - ADDITIONAL PHYSICAL FORM ENTRY
Relationship information Print version:
Main entry heading Hutter, Frank
Title Automated Machine Learning
Place, publisher, and date of publication Cham : Springer International Publishing AG,c2019
International Standard Book Number 9783030053178
797 2# - LOCAL ADDED ENTRY--CORPORATE NAME (RLIN)
Corporate name or jurisdiction name as entry element ProQuest (Firm)
830 #4 - SERIES ADDED ENTRY--UNIFORM TITLE
Uniform title The Springer Series on Challenges in Machine Learning Series
856 40 - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier <a href="https://ebookcentral.proquest.com/lib/bacm-ebooks/detail.action?docID=5788944">https://ebookcentral.proquest.com/lib/bacm-ebooks/detail.action?docID=5788944</a>
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