On the Usefulness of Similarity Based Projection Spaces for Transfer Learning

Abstract—We address the problem of domain adaptation for binary classification which arises when the distributions generating the source learning data and target test data are

For this purpose, we have investigated the algorithmic robustness notion proposed by Xu and Mannor [3] based on the following property: “If a testing sample is similar to a

The obtained results contain a divergence term between the two domains distributions that naturally appears when bounding the deviation between the same similarity’s performance on

Finally, the multi-class case and the semi-supervised domain adaptation (i.e when some labels are available on the target) are cru- cial extensions to add as they would allow us to

Upper bounding techniques for the reduced modelling error have been obtained for such problems in the context of the Reduced Basis Method.. In [16], the error estimation relies on

Starting Denavit-Hartenberg model (standard or modified DH) and generalized transformation matrix, the calculation accuracy and repeatability conducted in general and then applied

Re- call that, in Section 1.3.2 , we discussed the symbolic methods for isolating the singularities of plane curves in the context of solving zero-dimensional systems of