Machine Learning and Metaheuristics: Methods and Analysis
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Beschreibung
This book takes a balanced approach between theoretical understanding and real-time applications. All the topics included real-world problems which show how to explore, build, evaluate, and optimize machine learning models fusion with metaheuristic algorithms. Optimization algorithms classified into two broad categories as deterministic and probabilistic algorithms. The content of book elaborates optimization algorithms such as particle swarm optimization, ant colony optimization, whale search algorithm, and cuckoo search algorithm.
This book takes a balanced approach between theoretical understanding and real-time applications. All the topics included real-world problems which show how to explore, build, evaluate, and optimize machine learning models fusion with metaheuristic algorithms. Optimization algorithms classified into two broad categories as deterministic and probabilistic algorithms. The content of book elaborates optimization algorithms such as particle swarm optimization, ant colony optimization, whale search algorithm, and cuckoo search algorithm.
Provides rich set of chapters of machine learning and metaheuristic optimization Emphasizes optimization algorithms such as PSO, ant colony optimization, cuckoo search algorithm, etc. Includes real-world examples with attention to theoretical aspects for better understanding
Autor*in
Uma N. Dulhare
Themen in »Machine Learning and Metaheuristics: Methods and Analysis«
Combinational Optimization Feature Selection Swarm Intelligence Metaheuristics Algorithms Nature Inspired Algorithms