Semi-Supervised Learning for the Analysis of Unstructured Documents

The goal of this project is to develop and analyze methods for analyzing textual information. This should be realized by using semi-supervised learning methods, which use labeled as well as unlabeled data. In particular, existing methods which are already applied for pattern recognition should be adapted such that those can also be applied for textual data. For a practical analysis comparisons to SVM and k-NN classifier using a boosting algorithm should be performed, the influence of the amount of labeled/unlabeled data and the convergence should be analyzed. Moreover, a fair comparative study between batch and on-line methods is performed.