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Optimization algorithms in machine l...

Das, Debashish.

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Optimization algorithms in machine learning = a meta-heuristics perspective /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Optimization algorithms in machine learning/ by Debashish Das, Ali Safaa Sadiq, Seyedali Mirjalili.
其他題名:	a meta-heuristics perspective /
作者:	Das, Debashish.
其他作者:	Sadiq, Ali Safaa.
出版者:	Singapore :Springer Nature Singapore : : 2025.,
面頁冊數:	xvii, 181 p. :ill. (some col.), digital ;24 cm.
內容註:	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.
Contained By:	Springer Nature eBook
標題:	Machine learning. -
電子資源:	https://doi.org/10.1007/978-981-96-3849-9
ISBN:	9789819638499

Optimization algorithms in machine learning = a meta-heuristics perspective /
Das, Debashish.

Optimization algorithms in machine learninga meta-heuristics perspective /[electronic resource] :by Debashish Das, Ali Safaa Sadiq, Seyedali Mirjalili. - Singapore :Springer Nature Singapore :2025. - xvii, 181 p. :ill. (some col.), digital ;24 cm. - Engineering optimization: methods and applications,2731-4057. - Engineering optimization: methods and applications..

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.

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.

ISBN: 9789819638499

Standard No.: 10.1007/978-981-96-3849-9doiSubjects--Topical Terms:

533906
Machine learning.

LC Class. No.: Q325.5

Dewey Class. No.: 006.31

Optimization algorithms in machine learning = a meta-heuristics perspective /
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