Mapping MAP estimates of unidentifiable dynamical models to structurally identifiable parameter combinations improves time series classification generalization, especially with few training samples.
Multiclass sparse centroids with application to fast time series classification,
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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.