Toggle navigation sidebar
Toggle in-page Table of Contents
Machine Learning to predict RX localization
1. Introduction
1.1. Pedagogic context
1.2. Scientific context
1.3. Objectives
2. Data description
2.1. Experimental Data
2.2. Additional data
2.3. Data exploration
3. Pixel classification
3.1. Tree Based Classifiers
3.2. Neighbourhood based classifiers
3.3. Pixels classification conclusion
4. Triple junction classification
4.1. Support Vector Machine for Triple Junction dataset
4.2. Artificial Neural Network classifiers
4.3. Analysis
5. Discussion
6. Conclusion
7. Bibliography
8. Annexes
8.1. Compute and save anisotropy factors
8.2. Compute Triple Junction dataset
8.3. Computation pipeline of Dataset for CNN and Mixuture NN
8.4. Principal Components Analysis (PCA) on pixel dataset (with anisotropy)
8.5. Principal Components Analysis (PCA) on TJ dataset
8.6. t-distribued Stochastic Neighbor Embleding (t-SNE) on TJ dataset
8.7. Tresholding data for class balance
repository
open issue
Index