Metaheuristics for Machine Learning

• Titel: Metaheuristics for Machine Learning
• Titelzusatz: New Advances and Tools
• Mitwirkende: Eddaly, Mansour [HerausgeberIn]
• Mitwirkende: Jarboui, Bassem [HerausgeberIn]
• Mitwirkende: Siarry, Patrick [HerausgeberIn]
• Verf.angabe: Mansour Eddaly, Bassem Jarboui, Patrick Siarry, editors
• Verlagsort: Singapore
• Verlag: Springer
• E-Jahr: 2023
• Jahr: [2023]
• Umfang: v, 223 Seiten
• Illustrationen: Illustrationen, Diagramme
• Gesamttitel/Reihe: Computational Intelligence Methods and Applications
• ISBN: 978-981-19-3890-0
• Sprache: eng
• Bibliogr. Hinweis: Erscheint auch als : Online-Ausgabe: Metaheuristics for Machine Learning. - 1st ed. 2023.. - Singapore : Springer Nature Singapore, 2023. - 1 Online-Ressource(XV, 223 p. 1 illus.)
• Sach-SW: Artificial intelligence
• Sach-SW: COMPUTERS / Artificial Intelligence
• Sach-SW: COMPUTERS / Computer Science
• Sach-SW: Computer science
• Sach-SW: Künstliche Intelligenz
• Sach-SW: Machine learning
• Sach-SW: Maschinelles Lernen
• Sach-SW: Theoretische Informatik
• K10plus-PPN: 1883733618

Abstract

Using metaheuristics to enhance machine learning techniques has become trendy and has achieved major successes in both supervised (classification and regression) and unsupervised (clustering and rule mining) problems. Furthermore, automatically generating programs via metaheuristics, as a form of evolutionary computation and swarm intelligence, has now gained widespread popularity. This book investigates different ways of integrating metaheuristics into machine learning techniques, from both theoretical and practical standpoints. It explores how metaheuristics can be adapted in order to enhance machine learning tools and presents an overview of the main metaheuristic programming methods. Moreover, real-world applications are provided for illustration, e.g., in clustering, big data, machine health monitoring, underwater sonar targets, and banking