Handbook of Evolutionary Machine Learning
This book, written by leading international researchers of evolutionary approaches to machine learning, explores various ways evolution can address machine learning problems and improve current methods of machine learning. Topics in this book are organized into five parts. The first part introduces...
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| Ente Autore: | |
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| Altri autori: | , , |
| Natura: | Elettronico eBook |
| Lingua: | inglese |
| Pubblicazione: |
Singapore :
Springer Nature Singapore : Imprint: Springer,
2024.
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| Edizione: | 1st ed. 2024. |
| Serie: | Genetic and Evolutionary Computation,
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| Soggetti: | |
| Accesso online: | https://doi.org/10.1007/978-981-99-3814-8 |
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Sommario:
- Part 1. Overview chapters
- Chapter 1. EML Fundamentals
- Chapter 2. EML in Supervised Learning
- Chapter 3. EML in Unsupervised Learning
- Chapter 4. EML in Reinforcement Learning
- Part 2. Evolutionary Computation as Machine Learning
- Chapter 5. Evolutionary Clustering
- Chapter 6. Evolutionary Classification and Regression
- Chapter 7. Evolutionary Ensemble Learning
- Chapter 8. Evolutionary Deep Learning
- Chapter 9. Evolutionary Generative Models
- Part 3. Evolutionary Computation for Machine Learning
- Chapter 10. Evolutionary Data Preparation
- Chapter 11. Evolutionary Feature Engineering and Selection
- Chapter 12. Evolutionary Model Parametrization
- Chapter 13. Evolutionary Model Design
- Chapter 14. Evolutionary Model Validation
- Part 4. Applications
- Chapter 15. EML in Medicine
- Chapter 16. EML in Robotics
- Chapter 17. EML in Finance
- Chapter 18. EML in Science
- Chapter 19. EML in Environmental Science
- Chapter 20. EML in the Arts.