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This thesis explores approaches towards learning with kernel methods using prior knowledge. Invariant learning with kernel methods is considered in more details. In the first part of the thesis, kernels are developed which incorporate prior knowledge on invariant transformations. Next, algorithms which specifically include prior knowledge are considered. An algorithm which linearly classifies d...

The performance of a natural language system trained on one domain often drops significantly when testing on another domain. Therefore, the problem of domain adaptation remains one of the most important natural language processing challenges. While many different domain adaptation frameworks have been proposed, they have ignored one natural resource – the prior knowledge on the new domain. In t...