Logistic Regression as a Baseline Classification Algorithm

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Table of contents

Remerciements
contents
list of figures
list of tables
acronyms
1 introduction
1.1 Context
1.2 Motivations
1.3 Contributions and outline
1.4 Related publications
2 theoretical background
2.1 Introduction
2.2 Radar Signal and Simulation
2.3 Euclidean Machine Learning
2.4 Information Geometry
2.5 Riemannian Machine Learning
2.6 Conclusion
3 second-order pipeline for temporal classification
3.1 Introduction
3.2 Learning on structured time series representations
3.3 Full pipeline for temporal classification
3.4 Experimental validation
3.5 Conclusion
4 advances in spd neural networks
4.1 Introduction
4.2 Data-Aware Mapping Network
4.3 Batch-Normalized SPDNet
4.4 Riemannian manifold-constrained optimization
4.5 Convolution for covariance time series
4.6 Experimental validation
4.7 Conclusion
5 conclusion and perspectives
bibliography