Discriminative invariant alignment for unsupervised domain adaptation
As one of the most prevalent branches of transfer learning, domain adaptation is dedicated
to generalizing the knowledge of a source domain to a target domain to perform machine …
to generalizing the knowledge of a source domain to a target domain to perform machine …
Better-than-chance classification for signal detection
The estimated accuracy of a classifier is a random quantity with variability. A common
practice in supervised machine learning, is thus to test if the estimated accuracy is …
practice in supervised machine learning, is thus to test if the estimated accuracy is …
Differential effects of brain disorders on structural and functional connectivity
Different measures of brain connectivity can be defined based on neuroimaging read-outs,
including structural and functional connectivity. Neurological and psychiatric conditions are …
including structural and functional connectivity. Neurological and psychiatric conditions are …
Sensor-level maps with the kernel two-sample test
Traditional approaches to create sensor-level maps from magnetoencephalographic (MEG)
data rely on mass-univariate methods. In order to overcome some limitations of univariate …
data rely on mass-univariate methods. In order to overcome some limitations of univariate …
[引用][C] A time series two-sample test based on comparing distributions of pairwise distances
P Montero-Manso, JA Vilar - Proceedings of AALTD 2016: Second ECML/PKDD …, 2016