Metalearning [E-Book] : Applications to Automated Machine Learning and Data Mining / by Pavel Brazdil, Jan N. van Rijn, Carlos Soares, Joaquin Vanschoren.
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...
Saved in:
Full text |
|
Personal Name(s): | Brazdil, Pavel, author |
Soares, Carlos, author / Vanschoren, Joaquin, author / van Rijn, Jan N., author | |
Edition: |
2nd edition 2022. |
Imprint: |
Cham :
Springer,
2022
|
Physical Description: |
XII, 346 pages 90 illustrations, 45 illustrations in color (online resource) |
Note: |
englisch |
ISBN: |
9783030670245 |
DOI: |
10.1007/978-3-030-67024-5 |
Series Title: |
/* Depending on the record driver, $field may either be an array with
"name" and "number" keys or a flat string containing only the series
name. We should account for both cases to maximize compatibility. */?>
Cognitive Technologies
|
Subject (LOC): |
- 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.