  Recent developments in wearable devices have made accurate and efficient
seizure detection more important than ever. A challenge in seizure detection is
that patient-specific models typically outperform patient-independent models.
However, in a wearable device one typically starts with a patient-independent
model, until such patient-specific data is available. To avoid having to
construct a new classifier with this data, as required in conventional kernel
machines, we propose a transfer learning approach with a tensor kernel machine.
This method learns the primal weights in a compressed form using the canonical
polyadic decomposition, making it possible to efficiently update the weights of
the patient-independent model with patient-specific data. The results show that
this patient fine-tuned model reaches as high a performance as a
patient-specific SVM model with a model size that is twice as small as the
patient-specific model and ten times as small as the patient-independent model.
