Metaheuristics in Machine Learning: Theory and Applications

Released on by Diego Oliva
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  • Metaheuristics in Machine Learning: Theory and Applications Book Detail

  • Author : Diego Oliva
  • Release Date :
  • Publisher : Springer Nature
  • Genre : Computational intelligence
  • Pages : 765
  • ISBN 13 : 3030705420
  • File Size : 33,33 MB
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Metaheuristics in Machine Learning: Theory and Applications by Diego Oliva PDF Summary

Book Description: This book is a collection of the most recent approaches that combine metaheuristics and machine learning. Some of the methods considered in this book are evolutionary, swarm, machine learning, and deep learning. The chapters were classified based on the content; then, the sections are thematic. Different applications and implementations are included; in this sense, the book provides theory and practical content with novel machine learning and metaheuristic algorithms. The chapters were compiled using a scientific perspective. Accordingly, the book is primarily intended for undergraduate and postgraduate students of Science, Engineering, and Computational Mathematics and is useful in courses on Artificial Intelligence, Advanced Machine Learning, among others. Likewise, the book is useful for research from the evolutionary computation, artificial intelligence, and image processing communities.

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Metaheuristics for Machine Learning

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Metaheuristics for Machine Learning

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Using metaheuristics to enhance machine learning techniques has become trendy and has achieved major successes in both supervised (classification and regression