Table of Contents
Table of Contents
Preface
Acknowledgements
Chapter 1. An Introduction to Ensemble Learning in Research and Applications
(Yi-Tung Chan, PhD, Department of Electrical Engineering, R.O.C. Naval Academy, Kaohsiung, Taiwan)
Chapter 2. Ensemble Denoising Autoencoders: Ensemble Learning for a Noise Reduction
(Kazuki Sakai, Yuto Omae and Hirotaka Takahashi, Department of Electronic Control Engineering, National Institute of Technology, Nagaoka College, Nagaoka, Niigata, Japan, and others)
Chapter 3. Computer-Aided Diagnosis System for Bone Fracture Detection and Classification: A Review on Deep Learning Techniques
(Leonardo Tanzi, Enrico Vezzetti, Alessandro Aprato, Andrea Audisio and Alessandro Massè, Department of Management and Production Engineering, Politecnico di Torino, Torino, Italy, and others)
Chapter 4. Decision-Making Strategies with Clustering Based Unsupervised Learning for Smart Grids Planning
(Bogdan C. Neagu, PhD and Gheorghe Grigoraş, PhD, “Gheorghe Asachi” Technical University of Iasi, Faculty of Electrical Engineering, Iasi, Romania)
Chapter 5. Use of Ensemble Learning Techniques to Analyze Data Related to Education, Health and Standard of Living
(Juhi Gajwani and Pinaki Chakraborty, PhD, Department of Computer Science and Engineering, Netaji Subhas University of Technology, New Delhi, India)
Chapter 6. Quantitative Textural Measures of the Aeromagnetic Field: Two Examples at Regional Scale
(Mark Gettings, U.S. Geological Survey, Tucson, AZ, US)
Chapter 7. Medical Applications of Ensemble Learning
(Ciro Comparetto and Franco Borruto, Obstetrician and Gynecologist, Florence, Italy, and others)
Chapter 8. Ensemble Learning Approach in Automated Modal Identification
(Mahdi Ghamami, Hassan Nahvi and Vahid Yaghoubi, Department of Mechanical Engineering, Isfahan University of Technology, Isfahan, Iran, and others)
Index