Toller Maximilian
2018
Parameter-Free Collective Anomaly Detection in Sequential Data
Master
Anomaly detection is a common research topic in data science. Detecting
anomalies that occur collectively in a sequence is useful for many appli-
cations such as intrusion or fault detection. In this thesis, I developed a
parameter-free solution for detecting collective anomalies in sequential
data based on stationarity and volatility estimation (STAVE). The STAVE
algorithm extracts subsequences of a full sequence with a sliding win-
dow and clusters them according to a stationarity and volatility distance
function. Collective anomalies are then detected by extracting the longest
connected sequence within the smallest cluster. In a practical evaluation,
STAVE achieved results comparable to commonly used parametric alterna-
tives, while retaining low computational complexity and requiring no input
other than the sequence to be investigated.
Anthofer Daniel
2018
Ein Neuronal Netzwerk zur Extraktion von Information aus deutschen Texten
Master
Systems that extract information from natural language texts usually need
to consider language-dependent aspects like vocabulary and grammar.
Compared to the development of individual systems for different languages, development of multilingual information extraction (IE) systems has the potential to reduce cost and effort. One path towards IE from different languages is to port an IE system from one language to another. PropsDE is an open IE (OIE) system that has been ported from the English system PropS to the German language. There are only few OIE methods for German available. Our goal is to develop a neural network that mimics the rules of an existing rule-based OIE system. For that, we need to learn about OIE from German text. By performing an analysis and a comparison of the rule-based systems PropS and PropsDE, we can observe a step towards multilinguality, and we learn about German OIE. Then we present a deep-learning based OIE system for German, which mimics the behaviour of PropsDE. The precision in directly imitating PropsDE is 28.1%. Our model produces many extractions that appear promising, but are not fully correct
Moesslang Dominik
2018
Studying consumption patterns in online food communities
Master
Salihovic Jasmin
2018
On the predictability of online food items
Master
Adelmann Alexander
2018
Context Search Helper
Master
Milot Gashi
2018
Personalized Visualizations based on user's behavior
Master
Purgstaller Roman
2018
Dynamic N-Gram Based Feature Selection for Text Classification
Master
Feature selection has become an important focus in machine learning. Es-
pecially in the area of text classification, using n-gram language models
will lead to high dimensional datasets. In this thesis we propose a new
method of dimensionality reduction. Starting with a small subset of features,
an iterative forward selection method is performed to extend our feature
space. The main idea is, to interpret the results from a trained classifier
in order to determine feature importance. Our experimental results over
various classification algorithms show that with this approach it is possible
to improve prediction performance over other state of the art dimension
reduction methods, while providing a more cost-effective feature space.