Fusion of Machine Learning Paradigms : Theory and Applications / edited by Ioannis K. Hatzilygeroudis, George A. Tsihrintzis, Lakhmi C. Jain.

Format:

Book

Contributor:

Hatzilygeroudis, Ioannis, editor.

Tsihrintzis, George A., editor.

Jain, L. C., editor.

Series:

Intelligent Systems Reference Library, 1868-4408 ; 236

Language:

English

Subjects (All):

Computational intelligence.

Artificial intelligence.

Machine learning.

Data protection.

Computational Intelligence.

Artificial Intelligence.

Machine Learning.

Data and Information Security.

Local Subjects:

Computational Intelligence.

Artificial Intelligence.

Machine Learning.

Data and Information Security.

Physical Description:

1 online resource (204 pages)

Edition:

1st ed. 2023.

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2023.

Summary:

This book aims at updating the relevant computer science-related research communities, including professors, researchers, scientists, engineers and students, as well as the general reader from other disciplines, on the most recent advances in applications of methods based on Fusing Machine Learning Paradigms. Integrated or Hybrid Machine Learning methodologies combine together two or more Machine Learning approaches achieving higher performance and better efficiency when compared to those of their constituent components and promising major impact in science, technology and the society. The book consists of an editorial note and an additional eight chapters and is organized into two parts, namely: (i) Recent Application Areas of Fusion of Machine Learning Paradigms and (ii) Applications that can clearly benefit from Fusion of Machine Learning Paradigms. This book is directed toward professors, researchers, scientists, engineers and students in Machine Learning-related disciplines, as the hybridism presented, and the case studies described provide researchers with successful approaches and initiatives to efficiently address complex classification or regression problems. It is also directed toward readers who come from other disciplines, including Engineering, Medicine or Education Sciences, and are interested in becoming versed in some of the most recent Machine Learning-based technologies. Extensive lists of bibliographic references at the end of each chapter guide the readers to probe further into the application areas of interest to them.

Contents:

Editorial Note

Artificial Intelligence as Dual-Use Technology

Diabetic Retinopathy Detection using Transfer and Reinforcement Learning with effective image preprocessing and data augmentation techniques. .

Notes:

Includes bibliographical references.

Other Format:

Print version: Hatzilygeroudis, Ioannis K. Fusion of Machine Learning Paradigms

ISBN:

3-031-22371-3