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Advanced machine learning with evolutionary and metaheuristic techniques

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Advanced machine learning with evolutionary and metaheuristic techniquesedited by Jayaraman Valadi ... [et al.].
其他作者:	Valadi, Jayaraman.
出版者:	Singapore :Springer Nature Singapore :2024.
面頁冊數:	x, 362 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Machine learning.
電子資源:	https://doi.org/10.1007/978-981-99-9718-3
ISBN:	9789819997183$q(electronic bk.)

Advanced machine learning with evolutionary and metaheuristic techniques
Advanced machine learning with evolutionary and metaheuristic techniques[electronic resource] /edited by Jayaraman Valadi ... [et al.]. - Singapore :Springer Nature Singapore :2024. - x, 362 p. :ill., digital ;24 cm. - Computational intelligence methods and applications,2510-1773. - Computational intelligence methods and applications..

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.

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 maximum efficiency. Or unlock the strategies behind hyperparameter optimization to enhance your transfer learning algorithms, yielding remarkable outcomes. Or embark on an illuminating journey through evolutionary techniques designed for constructing deep-learning frameworks. The book also introduces an intelligent RPL attack detection system tailored for IoT networks. Explore a promising avenue of optimization by fusing Particle Swarm Optimization with Reinforcement Learning. It uncovers the indispensable role of metaheuristics in supervised machine learning algorithms. Ultimately, this book bridges the realms of evolutionary dynamic optimization and machine learning, paving the way for pioneering innovations in the field.

ISBN: 9789819997183$q(electronic bk.)

Standard No.: 10.1007/978-981-99-9718-3doiSubjects--Topical Terms:

188639
Machine learning.

LC Class. No.: Q325.5

Dewey Class. No.: 006.31

Advanced machine learning with evolutionary and metaheuristic techniques
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520 $a 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 maximum efficiency. Or unlock the strategies behind hyperparameter optimization to enhance your transfer learning algorithms, yielding remarkable outcomes. Or embark on an illuminating journey through evolutionary techniques designed for constructing deep-learning frameworks. The book also introduces an intelligent RPL attack detection system tailored for IoT networks. Explore a promising avenue of optimization by fusing Particle Swarm Optimization with Reinforcement Learning. It uncovers the indispensable role of metaheuristics in supervised machine learning algorithms. Ultimately, this book bridges the realms of evolutionary dynamic optimization and machine learning, paving the way for pioneering innovations in the field.
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