Stefan Wrobel focuses on Artificial intelligence, Machine learning, Visual analytics, Visualization and Data science. His Artificial intelligence research is multidisciplinary, relying on both Transformation and Transformation based learning. His studies deal with areas such as Algorithm, Information extraction and Graph as well as Machine learning.
His biological study spans a wide range of topics, including Event, Trajectory, Data visualization and Cluster analysis. His work deals with themes such as Conceptual framework and Global Positioning System, which intersect with Data science. His Kernel method study integrates concerns from other disciplines, such as Adjacency matrix, Cograph, 1-planar graph and Hamiltonian path.
Stefan Wrobel mostly deals with Artificial intelligence, Machine learning, Visual analytics, Data science and Data mining. His Artificial intelligence research incorporates themes from Statistical relational learning and Pattern recognition. The study of Machine learning is intertwined with the study of Transformation in a number of ways.
Visual analytics is a subfield of Visualization that Stefan Wrobel studies. He combines subjects such as Field and Information visualization with his study of Data science. The concepts of his Data mining study are interwoven with issues in Trajectory and Data set.
Stefan Wrobel spends much of his time researching Visual analytics, Artificial intelligence, Data science, Machine learning and Visualization. Stefan Wrobel has researched Visual analytics in several fields, including Theoretical computer science, Pairwise comparison, Workflow and Human–computer interaction. His Deep learning, Reinforcement learning and Artificial neural network study in the realm of Artificial intelligence interacts with subjects such as Automation and Selection.
His studies in Data science integrate themes in fields like Network embedding, Patent citation, Citation, Technological evolution and Software. His study on Machine learning is mostly dedicated to connecting different topics, such as Data visualization. He interconnects Topic model, Embedding, Data processing and Perception in the investigation of issues within Visualization.
His primary areas of investigation include Artificial intelligence, Visual analytics, Data space, Word and Information retrieval. In Artificial intelligence, he works on issues like Machine learning, which are connected to Data visualization. His study brings together the fields of Data science and Visual analytics.
His Word research integrates issues from Range, Visualization and Workflow. His work carried out in the field of Deep learning brings together such families of science as Contextual image classification, Reduction, Distributed computing and Code. His work carried out in the field of Reinforcement learning brings together such families of science as Stochastic optimization, Leverage and Parameterized complexity.
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On Graph Kernels: Hardness Results and Efficient Alternatives
Thomas Gärtner;Thomas Gärtner;Peter A. Flach;Stefan Wrobel.
conference on learning theory (2003)
An Algorithm for Multi-relational Discovery of Subgroups
european conference on principles of data mining and knowledge discovery (1997)
Geovisual analytics for spatial decision support: Setting the research agenda
G. Andrienko;N. Andrienko;P. Jankowski;D. Keim.
International Journal of Geographical Information Science (2007)
Active Hidden Markov Models for Information Extraction
Tobias Scheffer;Christian Decomain;Stefan Wrobel.
intelligent data analysis (2001)
Visual analytics tools for analysis of movement data
Gennady Andrienko;Natalia Andrienko;Stefan Wrobel.
Sigkdd Explorations (2007)
Cyclic pattern kernels for predictive graph mining
Tamás Horváth;Thomas Gärtner;Stefan Wrobel.
knowledge discovery and data mining (2004)
Visual Analytics of Movement
Gennady Andrienko;Natalia Andrienko;Peter Bak;Daniel Keim.
Efficient co-regularised least squares regression
Ulf Brefeld;Thomas Gärtner;Tobias Scheffer;Stefan Wrobel.
international conference on machine learning (2006)
Movement Data Anonymity through Generalization
Anna Monreale;Gennady Andrienko;Natalia Andrienko;Fosca Giannotti.
Transactions on Data Privacy (2010)
A conceptual framework and taxonomy of techniques for analyzing movement
G. Andrienko;N. Andrienko;P. Bak;D. Keim.
Journal of Visual Languages and Computing (2011)
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