Semantische Suche

In this thesis we will analyze and visualize semantics from massive document directories. The results will be displayed using the arXiv corpus, which contains domain-specific (computer science) papers of the past thirty years. The analysis will illustrate and give insight about past trends of document directories and how their relationships evolve over time.

When working with large data sets, in many situations one has to deals with a large set data from a single class and only few negative examples from other classes. Learning classifiers, which can assign data points to one of the groups, is known as one-class classification (OCC) or outlier detection.

The objective of this thesis is to develop and evaluate an active learning process to train an OCC. The process uses domain knowledge to reasonably adopt a prior distribution. Knowing that prior distribution, query strategies will be evaluated, which consider the certainty, more detailed the uncertainty, of the estimated class membership scorings. The integration of the prior distribution and the estimation of uncertainty, will be modeled using a gaussian process.

Verfahren der künstlichen Intelligenz auf der Grundlage neuronaler Netzwerke können genutzt werden, um viele der besonders aufwändigen Aufgaben schneller oder sogar besser zu lösen als herkömmliche Methoden. In dieser Arbeit wird ein neuartiges neuronales Netzwerk auf seine Fähigkeit hin untersucht, eine Software allein anhand der Pixeldaten ihrer Benutzeroberfläche zu testen. Des Weiteren wird ein Framework entwickelt, welches mithilfe von leistungsfähigen GPUs den Trainingsvorgang deutlich beschleunigen kann.

The recently introduced Monte Carlo Dependency Estimation (MCDE) framework defines the dependency between a set of variables as the expected value of a stochastic process performed on them. In practice, this expected value is approximated with an estimator which iteratively performs a set of Monte Carlo simulations. In this thesis, we propose several alternative estimators to approximate this expected value. They function in a more dynamic way and also leverage information from previous approximation iterations. Using both probability theory and experiments, we show that our new estimators converge much faster than the original one.