Metalearning Applications to Automated Machine Learning and Data Mining
This open access book offers a comprehensive and thorough introduction to almost all aspects of metalearning and automated machine learning (AutoML), covering the basic concepts and architecture, evaluation, datasets, hyperparameter optimization, ensembles and workflows, and also how this knowledge...
| Main Authors: | , , , |
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| Format: | eBook |
| Language: | English |
| Published: |
Cham
Springer International Publishing
2022, 2022
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| Edition: | 2nd ed. 2022 |
| Series: | Cognitive Technologies
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| Subjects: | |
| Online Access: | |
| Collection: | Springer eBooks 2005- - Collection details see MPG.ReNa |
Table of Contents:
- Introduction
- Part I, Basic Architecture of Metalearning and AutoML Systems
- Metalearning Approaches for Algorithm Selection I
- Evaluating Recommendations of Metalearning / AutoML Systems
- Metalearning Approaches for Algorithm Selection II
- Automating Machine Learning (AutoML) and Algorithm Configuration
- Dataset Characteristics (Metafeatures)
- Automating the Workflow / Pipeline Design
- Part II, Extending the Architecture of Metalearning and AutoML Systems
- Setting Up Configuration Spaces and Experiments
- Using Metalearning in the Construction of Ensembles
- Algorithm Recommendation for Data Streams
- Transfer of Metamodels Across Tasks
- Automating Data Science
- Automating the Design of Complex Systems
- Repositories of Experimental Results (OpenML)
- Learning from Metadata in Repositories