TU Wien Informatics

20 Years

Role

2015

2014

2013

  • UMAP: A Universal Layer for Schema Mapping Languages / Chertes, F., & Feinerer, I. (2013). UMAP: A Universal Layer for Schema Mapping Languages. In H. Decker, L. Lhotská, S. Link, J. Basl, & A. M. Tjoa (Eds.), Lecture Notes in Computer Science (pp. 349–363). Lecture Notes in Computer Science. https://doi.org/10.1007/978-3-642-40173-2_28
  • The textcat package for n-gram based text categorization in R / Hornik, K., Mair, P., Rauch, J., Geiger, W., Buchta, C., & Feinerer, I. (2013). The textcat package for n-gram based text categorization in R. Journal of Statistical Software, 52(6), 1–17. http://hdl.handle.net/20.500.12708/155579
  • Efficient large-scale configuration via integer linear programming / Feinerer, I. (2013). Efficient large-scale configuration via integer linear programming. Artificial Intelligence for Engineering Design, Analysis and Manufacturing, 27(1), 37–49. https://doi.org/10.1017/s0890060412000376
  • Beyond the schools of psychology 1: A digital analysis of Psychological Review, 1894-1903 / Green, C., Feinerer, I., & Burman, J. (2013). Beyond the schools of psychology 1: A digital analysis of Psychological Review, 1894-1903. Journal of the History of the Behavioral Sciences, 49(2), 167–189. http://hdl.handle.net/20.500.12708/155581
  • Class Diagrams with Equated Association Chains / Feinerer, I., Salzer, G., & Sisel, T. (2013). Class Diagrams with Equated Association Chains. In 2013 International Symposium on Theoretical Aspects of Software Engineering. 7th International Symposium on Theoretical Aspects of Software Engineering, Birmingham, EU. https://doi.org/10.1109/tase.2013.35
  • Lossless Horizontal Decomposition with Domain Constraints on Interpreted Attributes / Feinerer, I., Guagliardo, P., & Franconi, E. (2013). Lossless Horizontal Decomposition with Domain Constraints on Interpreted Attributes. In G. Gottlob, G. Grasso, D. Olteanu, & C. Schallhart (Eds.), Big Data (pp. 77–91). Lecture Notes in Computer Science. https://doi.org/10.1007/978-3-642-39467-6_10

2012

  • A tm Plug-In for Distributed Text Mining in R / Theußl, S., Feinerer, I., & Hornik, K. (2012). A tm Plug-In for Distributed Text Mining in R. Journal of Statistical Software, 51(5), 1–31. http://hdl.handle.net/20.500.12708/164399
  • Spherical k-Means Clustering / Hornik, K., Feinerer, I., Kober, M., & Buchta, C. (2012). Spherical k-Means Clustering. Journal of Statistical Software, 50(10), 1–22. http://hdl.handle.net/20.500.12708/164398
  • Towards hybrid techniques for efficient declarative configuration / Feinerer, I. (2012). Towards hybrid techniques for efficient declarative configuration. In Proceedings of the Workshop on Configuration at ECAI 2012 (pp. 27–30). http://hdl.handle.net/20.500.12708/54424
  • Configuration Repair via Flow Networks / Salzer, G., Feinerer, I., & Sisel, T. (2012). Configuration Repair via Flow Networks. In C. Li & A. Felfernig (Eds.), Lecture Notes in Computer Science (pp. 321–330). https://doi.org/10.1007/978-3-642-34624-8_37

2011

2010

  • A New SNA Centrality Measure Quantifying the Distance to the Nearest Center / Bohn, A., Theußl, S., Feinerer, I., Hornik, K., Mair, P., & Walchhofer, N. (2010). A New SNA Centrality Measure Quantifying the Distance to the Nearest Center. In H. Locarek-Junge & C. Weihs (Eds.), Studies in Classification, Data Analysis, and Knowledge Organization (pp. 579–586). Springer. https://doi.org/10.1007/978-3-642-10745-0_63
  • Computing Product Configurations via UML and Integer Linear Programming / Falkner, A., Feinerer, I., Salzer, G., & Schenner, G. (2010). Computing Product Configurations via UML and Integer Linear Programming. International Journal of Mass Customisation, 3(4), 351. https://doi.org/10.1504/ijmassc.2010.037650
  • Kernel-based machine learning for fast text mining in R / Karatzoglou, A., & Feinerer, I. (2010). Kernel-based machine learning for fast text mining in R. Computational Statistics & Data Analysis, 54(2), 290–297. https://doi.org/10.1016/j.csda.2009.09.023
  • Support Vector Machines for Large Scale Text Mining in R / Feinerer, I., & Karatzoglou, A. (2010). Support Vector Machines for Large Scale Text Mining in R. In Y. Lechevallier & G. Saporta (Eds.), Proceedings of COMPSTAT’2010 19th International Conference on Computational StatisticsParis France, August 22-27, 2010 Keynote, Invited and Contributed Papers (pp. 991–998). Physica. http://hdl.handle.net/20.500.12708/53342
  • Analysis and Algorithms for Stemming Inversion / Feinerer, I. (2010). Analysis and Algorithms for Stemming Inversion. In P.-J. Cheng, M.-Y. Kan, W. Lam, & P. Nakov (Eds.), Information Retrieval Technology (pp. 290–299). Springer. https://doi.org/10.1007/978-3-642-17187-1_28

2009

2008

  • Text Mining Infrastructure in R / Feinerer, I., Hornik, K., & Meyer, D. (2008). Text Mining Infrastructure in R. Journal of Statistical Software, 25(5), 54. http://hdl.handle.net/20.500.12708/170772
  • Text Mining of Supreme Administrative Court Jurisdictions / Feinerer, I., & Hornik, K. (2008). Text Mining of Supreme Administrative Court Jurisdictions. In Data Analysis, Machine Learning and Applications (pp. 569–576). Springer. https://doi.org/10.1007/978-3-540-78246-9_67
  • Fast text mining using kernels in R / Feinerer, I., & Karatzoglou, A. (2008). Fast text mining using kernels in R. In COMPSTAT 2008-Proceedings in Computational Statistics (p. 8). http://hdl.handle.net/20.500.12708/52445
  • Solving Practical Configuration Problems using UML / Falkner, A., Feinerer, I., Salzer, G., & Schenner, G. (2008). Solving Practical Configuration Problems using UML. In Proceedings of ECAI 2008 Workshop on Configuration Systems (p. 6). http://hdl.handle.net/20.500.12708/52363

2007

2006

2005

 

  • INiTS Award
    2007 / INiTS-Award / Austria

Soon, this page will include additional information such as reference projects, activities as journal reviewer and editor, memberships in councils and committees, and other research activities.

Until then, please visit Ingo Feinerer’s research profile in TISS .