Mapping MAP estimates of unidentifiable dynamical models to structurally identifiable parameter combinations improves time series classification generalization, especially with few training samples.
Reservoir computing approaches for representation and classification of multivari- ate time series,
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On the importance of structural identifiability for machine learning with partially observed dynamical systems
Mapping MAP estimates of unidentifiable dynamical models to structurally identifiable parameter combinations improves time series classification generalization, especially with few training samples.