Optimization Algorithms in Machine Learning
A Meta-heuristics Perspective
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Beschreibung
This book explores the development of several new learning algorithms that utilize recent optimization techniques and meta-heuristics. It addresses well-known models such as particle swarm optimization, genetic algorithm, ant colony optimization, evolutionary strategy, population-based incremental learning, and grey wolf optimizer for training neural networks. Additionally, the book examines the challenges associated with these processes in detail. This volume will serve as a valuable reference for individuals in both academia and industry.
This book explores the development of several new learning algorithms that utilize recent optimization techniques and meta-heuristics. It addresses well-known models such as particle swarm optimization, genetic algorithm, ant colony optimization, evolutionary strategy, population-based incremental learning, and grey wolf optimizer for training neural networks. Additionally, the book examines the challenges associated with these processes in detail. This volume will serve as a valuable reference for individuals in both academia and industry.
Focuses of the development of several new learning algorithms using recent optimization algorithms and meta-heuristics Discusses widespread models like Particle Swarm Optimization (PSO), Genetic Algorithm (GA), etc Useful reference to those in academia and industry
Autor*in
Debashish Das
Themen in »Optimization Algorithms in Machine Learning«
Neural Networks Optimization Training Neural Networks Supervised Learning Evolutionary Algorithm Meta-heuristics Swarm Intelligence Hybrid Intelligent Systems Genetic Algorithms Particle Swarm Optimization Ant Colony Optimization Grey Wolf Optimizer Stock Prediction