Online system identification is the estimation of parameters of a dynamical system, such as mass or friction coefficients, for each measurement of the input and output signals.
We address the problem of online Bayesian state and parameter tracking in autoregressive (AR) models with time-varying process noise variance.
In a broad range of fields it may be desirable to reuse a supervised classification algorithm and apply it to a new data set.
Here we present a smoothness prior that is fit to segmentations produced at another medical center.
Feature-based methods revolve around on mapping, projecting and representing features such that a source classifier performs well on the target domain and inference-based methods incorporate adaptation into the parameter estimation procedure, for instance through constraints on the optimization procedure.
Domain adaptation and transfer learning are sub-fields within machine learning that are concerned with accounting for these types of changes.
Generalization of voxelwise classifiers is hampered by differences between MRI-scanners, e. g. different acquisition protocols and field strengths.
For sample selection bias settings, and for small sample sizes, the importance-weighted risk estimator produces overestimates for datasets in the body of the sampling distribution, i. e. the majority of cases, and large underestimates for data sets in the tail of the sampling distribution.
Cross-validation under sample selection bias can, in principle, be done by importance-weighting the empirical risk.
Due to this acquisition related variation, classifiers trained on data from a specific scanner fail or under-perform when applied to data that was acquired differently.
This paper identifies a problem with the usual procedure for L2-regularization parameter estimation in a domain adaptation setting.
Our empirical evaluation of FLDA focuses on problems comprising binary and count data in which the transfer can be naturally modeled via a dropout distribution, which allows the classifier to adapt to differences in the marginal probability of features in the source and the target domain.