Description
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.
Table of Contents
Challenges and opportunities in Machine Learning using optimization techniques.- Optimization methods: traditional versus stochastic.- Heuristic and meta-heuristic optimization algorithms.- A comprehensive review of evolutionary algorithms and swarm intelligence methods.- Artificial Neural Networks: structure and learning.- A survey of Neural Networks trained by optimization algorithms and meta-heuristics.
-
- 洋書電子書籍
- Scaling Enterprise …
-
- 洋書電子書籍
-
ソーシャルワークと裁判（第３版）
