On Graph Kernels: Hardness Results and Efficient Alternatives
- Thomas Gärtner, Peter A. Flach, S. Wrobel
- Computer ScienceAnnual Conference Computational Learning Theory
- 2003
As most ‘real-world’ data is structured, research in kernel methods has begun investigating kernels for various kinds of structured data, but only very specific graphs such as trees and strings have been considered.
An Algorithm for Multi-relational Discovery of Subgroups
- S. Wrobel
- Computer ScienceEuropean Conference on Principles of Data Mining…
- 24 June 1997
An algorithm is described for finding statistically unusual subgroups in a multi-relation database that uses optimistic estimate and minimal support pruning, an optimal refinement operator and sampling to ensure efficiency and can easily be parallelized.
Active Hidden Markov Models for Information Extraction
- T. Scheffer, Christian Decomain, S. Wrobel
- Computer ScienceInternational Symposium on Intelligent Data…
- 13 September 2001
This paper considers the more challenging task of learning hidden Markov models (HMMs) when only partially (sparsely) labeled documents are available for training, and describes an EM style algorithm for learning HMMs from partially labeled data.
Geovisual analytics for spatial decision support: Setting the research agenda
- G. Andrienko, N. Andrienko, S. Wrobel
- Computer ScienceInternational Journal of Geographical Information…
- 8 January 2007
This article summarizes the results of the workshop on Visualization, Analytics & Spatial Decision Support, which took place at the GIScience conference in September 2006, and suggests a new research direction ‘Geovisual Analytics for Spatial decision Support’, which emphasizes the importance of visualization and interactive visual interfaces and the link with the emerging research discipline of Visual Analytics.
Cyclic pattern kernels for predictive graph mining
- T. Horváth, Thomas Gärtner, S. Wrobel
- Computer ScienceKnowledge Discovery and Data Mining
- 22 August 2004
The experimental results show that cyclic pattern kernels can be computed quickly and offer predictive performance superior to recent graph kernels based on frequent patterns.
Visual analytics tools for analysis of movement data
- G. Andrienko, N. Andrienko, S. Wrobel
- Computer ScienceSKDD
- 1 December 2007
It is argued that by using the right visual analytics tools for the analysis of massive collections of movement data, it is possible to effectively support human analysts in understanding movement behaviors and mobility patterns.
Proceedings of the 22nd international conference on Machine learning
- S. Džeroski, L. D. Raedt, S. Wrobel
- Computer Science
- 7 August 2005
This volume, which is also available online from http://www.machinelearning.org, contains the papers accepted for presentation at ICML-2005, the 22nd lnternational Conference on Machine Learning,…
Transformation-Based Learning Using Multirelational Aggregation
- Mark-A. Krogel, S. Wrobel
- Computer ScienceInternational Conference on Inductive Logic…
- 9 September 2001
This paper builds on popular transformation-based approaches to ILP and describes how they can naturally be extended with relational aggregation and results in a multirelational learner that outperforms a structurally-oriented ILP system both in speed and accuracy on this class of problems.
A conceptual framework and taxonomy of techniques for analyzing movement
- G. Andrienko, N. Andrienko, Peter Bak, D. Keim, S. Kisilevich, S. Wrobel
- Computer ScienceJournal of Visual Languages and Computing
- 1 June 2011
Tight Optimistic Estimates for Fast Subgroup Discovery
- H. Grosskreutz, S. Rüping, S. Wrobel
- Computer ScienceECML/PKDD
- 15 September 2008
This paper shows that optimistic estimate pruning can be developed into a sound and highly effective pruning approach for subgroup discovery and presents tight optimistic estimates for the most popular binary and multi-class quality functions, and presents a family of increasingly efficient approximations to these optimal functions.
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