Michal Skubacz

13 papers B 3C 1Journal 1Unranked 8
YearRankTypeTitle / Venue / Authors
2008 — conf
Mining and Exploring Unstructured Customer Feedback Data Using Language Models and Treemap Visualizations.
Web Intelligence
Cai-Nicolas Ziegler, Michal Skubacz, Maximilian Viermetz
2008 — conf
Tracking Topic Evolution in News Environments.
CEC/EEE
Maximilian Viermetz, Michal Skubacz, Cai-Nicolas Ziegler, Dietmar Seipel
2007 — conf
Content Extraction from News Pages Using Particle Swarm Optimization on Linguistic and Structural Features.
Web Intelligence
Cai-Nicolas Ziegler, Michal Skubacz
2007 J jnl
Topic Trend Detection in Newsgroups.
Künstliche Intell.
Bettina Hoser, Jan Schröder, Andreas Geyer-Schulz, Maximilian Viermetz, Michal Skubacz
2007 — conf
Using Topic Discovery to Segment Large Communication Graphs for Social Network Analysis.
Web Intelligence
Maximilian Viermetz, Michal Skubacz
2006 — conf
Relevance and Impact of Tabbed Browsing Behavior on Web Usage Mining.
Web Intelligence
Maximilian Viermetz, Carsten Stolz, Vassil Gedov, Michal Skubacz
2006 — conf
Towards Automated Reputation and Brand Monitoring on the Web.
Web Intelligence
Cai-Nicolas Ziegler, Michal Skubacz
2005 — conf
Guidance Performance Indicator - Web Metrics for Information Driven Web Sites.
Web Intelligence
Carsten Stolz, Maximilian Viermetz, Michal Skubacz, Ralph Neuneier
2005 B conf
Improving Semantic Consistency of Web Sites by Quantifying User Intent.
ICWE
Carsten Stolz, Maximilian Viermetz, Michal Skubacz, Ralph Neuneier
2005 B conf
Web Performance Indicator by Implicit User Feedback - Application and Formal Approach.
WISE
Michael N. Barth, Michal Skubacz, Carsten Stolz
2004 — conf
Matching web site structure and content.
WWW (Alternate Track Papers & Posters)
Vassil Gedov, Carsten Stolz, Ralph Neuneier, Michal Skubacz, Dietmar Seipel
2004 B conf
Measuring Semantic Relations of Web Sites by Clustering of Local Context.
ICWE
Carsten Stolz, Vassil Gedov, Kai Yu, Ralph Neuneier, Michal Skubacz
2000 C conf
Quantization of Continuous Input Variables for Binary Classification.
IDEAL
Michal Skubacz, Jaakko Hollmén