Finding domain invariant features is critical for successful domain adaptation and transfer learning. However, in the case of unsupervised adaptation, there is a significant risk of overfitting on source training data. Recently, a regularization for domain adaptation was proposed for deep models by . We build on their work by suggesting a more appropriate regularization for denoising autoencoders. Our model remains unsupervised and can be computed in a closed form. On standard text classification adaptation tasks, our approach yields the state of the art results, with an important reduction of the learning cost.
To facilitate effective search on the World Wide Web, meta search engines have been developed which do not search the Web themselves, but use available search engines to find the required information. By means of wrappers, meta search engines retrieve information from the pages returned by search engines. We present an approach to automatically create such wrappers by means of an incremental grammar induction algorithm. The algorithm uses an adaptation of the string edit distance. Our method performs well; it is quick, can be used for several types of result pages and requires a minimal amount of user interaction.
We address the problem of tight XML schemas and propose regular tree automata to model XML data. We show that the tree automata model is more powerful that the XML DTDs and is closed under main algebraic operations. We introduce the XML query algebra based the tree automata model, and discuss the query optimization and query pruning techniques. Finally, we show the conversion of tree automata schema into XML DTDs.
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