Advanced Machine Learning with Evolutionary and Metaheuristic Techniques
This book delves into practical implementation of evolutionary and metaheuristic algorithms to advance the capacity of machine learning. The readers can gain insight into the capabilities of data-driven evolutionary optimization in materials mechanics, and optimize your learning algorithms for maxim...
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| Համատեղ հեղինակ: | |
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| Այլ հեղինակներ: | , , , |
| Ձևաչափ: | Էլեկտրոնային էլ․ գիրք |
| Լեզու: | անգլերեն |
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Singapore :
Springer Nature Singapore : Imprint: Springer,
2024.
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| Հրատարակություն: | 1st ed. 2024. |
| Շարք: | Computational Intelligence Methods and Applications,
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| Խորագրեր: | |
| Առցանց հասանելիություն: | https://doi.org/10.1007/978-981-99-9718-3 |
| Ցուցիչներ: |
Ավելացրեք ցուցիչ
Չկան պիտակներ, Եղեք առաջինը, ով նշում է այս գրառումը!
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Բովանդակություն:
- Chapter 1. From Evolution to Intelligence: Exploring the Synergy of Optimization and Machine Learning
- Chapter 2. Metaheuristic and Evolutionary Algorithms in Ex-plainable Artificial Intelligence
- Chapter 3. Evolutionary Dynamic Optimization and Machine Learning
- Chapter 4. Evolutionary Techniques in making Efficient Deep-Learning Framework: A Review
- Chapter 5. Integrating Particle Swarm Optimization with Reinforcement Learning: A Promising Approach to Optimization
- Chapter 6. Synergies between Natural Language Processing and Swarm Intelligence Optimization: A Comprehensive Overview
- Chapter 7. Heuristics-based Hyperparameter Tuning for Transfer Learning Algorithms
- Chapter 8. Machine Learning Applications of Evolutionary and Metaheuristic Algorithms
- Chapter 9. Machine Learning Assisted Metaheuristic Based Optimization of Mixed Suspension Mixed Product Removal Process
- Chapter 10. Machine Learning based Intelligent RPL Attack Detection System for IoT Networks
- Chapter 11. Shallow and Deep Evolutionary Neural Networks applications in Solid Mechanics
- Chapter 12. Polymer and nanocomposite Informatics: Recent Applications of Artificial Intelligence and Data Repositories
- Chapter 13. Synergistic combination of machine learning and evolutionary and heuristic algorithms for handling imbalance in biological and biomedical datasets.