We begin where others give up.

Publications

Seewald A.K., Cypser J., Mendenhall A., Johnson T.: Clustering Caenorhabditis elegans 2D Expression Patterns. Technical Report, Seewald Solutions, Wien, 2009.
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Seewald A.K.: Automatic Extraction of Go Game Positions from Still Images: A Multi-Strategical Approach to Constained Multi-Object Recognition. Applied Artificial Intelligence 24(4), 2010.
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Seewald A.K., Gansterer W.N.: On the Detection and Identification of Botnets. Computers & Security, Volume 29, Issue 1, February 2010, Pages 45-58.
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Heindl A., Ecker R., Steiner G., Bises G., Thalhammer T., Rogojanu R., Uhrova H., Helmer H., Ellinger E., Seewald A.: Automated cell-detection technologies for science and diagnostics. Placenta 30 (9) 2009: P06.14.
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Heindl A., Ecker R., Steiner G., Bises G., Ellinger I., Thalhammer T., Fuchs R., Uhrova H., Seewald A.: Automated cell-detection technologies for science and diagnostics. Poster presentation at the 5th PhD Symposium of the Young Scientist Association of the Medical University of Vienna, June 2009, Vienna, Austria.
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Seewald A.K.: On the Brittleness of Handwritten Digit Recognition Models. Technical Report, Seewald Solutions, Wien, 2009.
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Seewald A.K.: Towards Automating Malware Classification and Characterization. In Konferenzband der 4. Jahrestagung des Fachbereichs Sicherheit der Gesellschaft für Informatik (german-language proceedings), Saarbrücken, April 2008, pp. 291-302.
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Seewald A.K., Kleedorfer F.: An Approximation of the String Subsequence Kernel for Practical SVM Classification and Redundancy Clustering, Journal for Advances in Data Analysis and Classification, Vol. 1, Number 3 / December 2007, pp. 221-239, DOI: 10.1007/s11634-007-0012-1.
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Seewald A.K.: An Evaluation of Naive Bayes Variants in Content-Based Learning for Spam Filtering, Intelligent Data Analysis 11(5), pp. 497-524, 2007.
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Seewald A.K.: Improving the Effectiveness of Mailings by Building a Response Model for Inactive Customers. Technical Report, Seewald Solutions, Wien, 2007.
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Seewald A.K., Holzbaur C., Widmer G.: Evaluation of Term Utility Functions for Very Short Multi-Document Summaries. Applied Artificial Intelligence 20(1), January 2006, pp.57-78.
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Pillet V., Zehnder M., Seewald A.K., Veuthey A-L, and Petrak J. GPSDB: a new database for synonyms expansion of gene and protein names. Bioinformatics 2005 21: 1743-1744.
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Dehaspe L., Attwood T.K., Daelemans W. et al. BioMinT: the Research Assistant for Biological Text Mining. Knowledge for Growth 2005, Gent, 3rd of June, 2005.
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Seewald A.K.: An Evaluation of Naive Bayes Variants in Content-Based Learning for Spam Filtering. Technical Report, Österreichisches Forschungsinstitut für Artificial Intelligence, Wien, TR-2005-20, 2005.
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Seewald A.K., Kleedorfer F.: Lambda Pruning - An Approximation of the String Subsequence Kernel. Technical Report, Österreichisches Forschungsinstitut für Artificial Intelligence, Wien, TR-2005-13, 2005.
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Seewald A.K.: Digits - A Dataset for Handwritten Digit Recognition. Technical Report, Österreichisches Forschungsinstitut für Artificial Intelligence, Wien, TR-2005-27, 2005.
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Seewald A.K.: A Close Look at Current Approaches in Spam Filtering. Technical Report, Österreichisches Forschungsinstitut für Artificial Intelligence, Wien, TR-2005-04, 2005.
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Seewald A.K.: Combining Bayesian and Rule Score Learning: Automated Tuning for SpamAssassin. Technical Report, Österreichisches Forschungsinstitut für Artificial Intelligence, Wien, TR-2004-11, 2004.
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Seewald A.K.: Ranking for Medical Annotation: Investigating Performance, Local Search and Homonymy Recognition. Proceedings of the Symposium on Knowledge Exploration in Life Science Informatics (KELSI 2004), Milano, Italy.
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Seewald A.K.: The Intelligent Go Board project. Folder, Seewald Solutions, March 2003.
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Seewald A.K.: Recognizing Domain and Species from MEDLINE Proteomics Publications. Workshop on Data Mining and Text Mining for Bioinformatics, 14th European Conference on Machine Learning (ECML-2003), Dubrovnik-Cavtat, Croatia, 2003.
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Seewald A.K.: Evaluating Protein Name Recognition: An Automatic Approach. Workshop on Data Mining and Text Mining for BioInformatics, 14th European Conference on Machine Learning (ECML-2003), Dubrovnik-Cavtat, Croatia, 2003.
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Seewald A.K.: Towards Understanding Stacking. PhD Thesis, Vienna University of Technology, 2003.
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Seewald A.K.: Towards a Theoretical Framework for Ensemble Classification. Poster paper, 2 pages. In Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence (IJCAI-03), Morgan Kaufmann, 2003.
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Seewald A.K.: Exploring the Parameter State Space of Stacking. In Proceedings of International Conference on Data Mining (ICDM-2002), Maebashi TERRSA, Maebashi City, Japan. IEEE Computer Society Press, Los Alamitos, California.
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Seewald A.K.: Meta-Learning for Stacked Classification. In Proceedings of the Second International Workshop on Integration and Collaboration Aspects of Data Mining, Decision Support and Meta-Learning (IDDM-2002), University of Helsinki, Department of Computer Science, Report B-2002-3.
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Seewald A.K.: How to Make Stacking Better and Faster While Also Taking Care of an Unknown Weakness. In Proceedings of the Nineteenth International Conference on Machine Learning (ICML-2002). Sydney, Australia. Morgan Kaufmann Publishers, San Francisco.
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Seewald A.K., Holzbaur C., Widmer G.: Offline Evaluation of Term Utility Functions. Technical Report, Österreichisches Forschungsinstitut für Artificial Intelligence, Wien, TR-2002-34, 2002.
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Seewald A.K.: Entertainment Robots - Myth Or Reality. In Proceedings of the 14th International FLAIRS Conference (FLAIRS-2001), AAAI Press, Menlo Park, California.
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Seewald A.K., Petrak J., Widmer G.: Hybrid Decision Tree Learners with Alternative Leaf Classifiers: An Empirical Study. In Proceedings of the 14th International FLAIRS Conference (FLAIRS-2001), AAAI Press, Menlo Park, California.
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Seewald A.K., Fürnkranz J.: An Evaluation of Grading Classifiers. In Advances in Intelligent Data Analysis: Proceedings of the 4th International Symposium (IDA-01). Lisbon, Portugal. Springer-Verlag 2001.
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Seewald A.K.: CoIL Challenge 2000 - Submitted Solution. In P. van der Putten, M. van Someren (eds.), CoIL Challenge 2000: The Insurance Company Case, LIACS Technical Report 2000-09, Leiden Institute of Advanced Computer Science, Leiden, published by Sentient Machine Research, Amsterdam.
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