We begin where others give up.

Publications

Heindl A, Dekan S, Ellinger I, Seewald A: Towards a Versatile Automated Cell-Detection System for Science and Diagnostics. Proceedings of the 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Buenos Aires, Argentina, p. 3045-3048, IEEE Catalog Number: CFP10EMB-DVD, ISBN: 978-1-4244-4124-2, ISSN: 1557-170X, 2010.
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Seewald AK, Cypser J, Mendenhall A, Johnson T (2010) Quantifying Phenotypic Variation in Isogenic Caenorhabditis elegans Expressing Phsp-16.2::gfp by Clustering 2D Expression Patterns, PLoS ONE 5(7): e11426. doi:10.1371/journal.pone.0011426.
Link to Open Access version at PLoS One Website & software

Rogojanu R., Mesteri I., Ellinger I., Thalhammer T., Kallay E., Heindl A., Seewald A., Bises G.: Characterization and Quantification of Macrophages in Colorectal Cancer by an Automated Cell Detection System. Poster presentation at the 6th PhD Symposium of the Young Scientist Assocation of the Medical University of Vienna, June 2010, Vienna, Austria.
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Heindl A., Pohl V., Rogojanu R., Ecker R., Thalhammer T., Bises G., Seewald A., Ellinger I.: Towards a Versatile Automated Cell-Detection System for Science and Diagnostics exemplified through receptor for advanced glycated end-products (RAGE) quantification in placental chorionic villi. Poster presentation at the 6th PhD Symposium of the Young Scientist Association of the Medical University of Vienna, June 2010, Vienna, Austria.
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Seewald A.K.: Automatic Extraction of Go Game Positions from Still Images: A Multi-Strategical Approach to Constrained Multi-Object Recognition. Applied Artificial Intelligence 24(3), 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.
PDF Link to project page

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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