
Introduction to Approaches and Modern Applications with Ensemble Learning
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Content
- Intro
- Contents
- Preface
- Acknowledgments
- Chapter 1
- An Introduction to Ensemble Learning in Research and Applications
- Abstract
- 1. Introduction
- 2. What Is Ensemble Learning?
- 3. How does Ensemble Learning Work?
- 3.1. Bagging
- 3.2. Boosting [10]
- 3.3. Stacking
- 4. Where Can Ensemble Learning Be Applied?
- Conclusion
- References
- Chapter 2
- Ensemble Denoising Autoencoders: Ensemble Learning for a Noise Reduction
- Chapter 3
- Computer-Aided Diagnosis System for Bone Fracture Detection and Classification: A Review on Deep Learning Techniques
- Abstract
- 1. Introduction
- 2. Deep Learning Scenario
- 3. Methodology
- 4. Approaches
- Discussion and Conclusion
- References
- Chapter 4
- Decision-Making Strategies with Clustering Based Unsupervised Learning for Smart Grids Planning
- Abstract
- Acronyms
- 1. Introduction
- 2. Clustering in Data Mining to Knowledge Extraction
- 2.1. General Aspects
- 2.2. Stages of the Clustering Process
- 2.3. Clustering Methods
- 2.4. The Validation of Clustering Results
- 3. The Load Profiling in the Electric Distribution Networks
- 3.1. The Load Profiling Process
- 3.1. Data Pre-Processing Stage
- 3.1.1. The Analisys of Big Data
- 3.1.2. Outlier Detection Using a Statistical Deep Learning Based Data Mining Approach
- 3.2. Normalization
- 3.3. Clustering Process
- 4. Energy Losses Estimation for Smart Grids Planning Based on Deep Learning Methods
- 4.1. Smart Grids Planning. Introduction
- 4.2. The Active Losses Forecasting Using a Deep Learning-Based Expert System
- 4.3. The Influence of Outliers on Active Losses Forecasting on Power Transformers Level
- Conclusion
- References
- Chapter 5
- Use of Ensemble Learning Techniques to Analyze Data Related to Education, Health and Standard of Living
- Abstract
- Introduction
- Related Work
- Methodology
- Data Sets
- Data Analysis
- Bagging
- Boosting
- Voting
- Random Forest
- Analysis-Related Results
- Health Dimension
- Education Dimension
- Standard of Living Dimension
- Prediction-Related Results
- IMR Predictions
- MMR Predictions
- TFR Predictions
- Discussion
- Conclusion
- References
- Chapter 6
- Quantitative Textural Measures of the Aeromagnetic Field: Two Examples at Regional Scale
- Chapter 7
- Medical Applications of Ensemble Learning
- Abstract
- Introduction
- Historical Overview
- State-of-the-Art
- Medical Applications
- Introduction
- Digital Healthcare
- Diagnostic Imaging
- Genomics
- Computer-Aided Diagnostics
- Bioinformatics
- Obstetric and Gynecological Applications
- Introduction
- Assisted Reproductive Technologies
- Prenatal Diagnosis
- Stillbirth and Preterm Birth
- Nutrition
- Gynecological Oncology
- Lights and Shadows
- Conclusion
- References
- Chapter 8
- Ensemble Learning Approach in Automated Modal Identification
- Abstract
- 1. Introduction
- 2. Background
- 3. Dissemination and Theoretical Considerations
- 3.1. Ensemble Learning
- 3.2. Dempster-Shafer Theory
- 3.3. Modal Identification
- 3.4. Uncertainty
- 4. Ensemble Modal Identification
- 5. Case Studies
- 5.1. Concrete Arch Dam
- 5.2. Cooling Fan
- Conclusion
- Acknowledgments
- References
- Biographical Sketch
- About the Editor
- Index
- Blank Page
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