Friedrich Leisch
University of Natural Resources and Life Sciences, Vienna
230 Papers
917 Citations
Friedrich Leisch is an academic researcher from University of Natural Resources and Life Sciences, Vienna. The author has contributed to research in topics: Market segmentation & Computer science. The author has an hindex of 49, co-authored 219 publications. Previous affiliations of Friedrich Leisch include Vienna University of Technology & University of Erlangen-Nuremberg.
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Papers
Using graphical statistics to better understand market segmentation solutions
Sara Dolnicar,Friedrich Leisch +1 more
TL;DR: This paper developed novel ways of visualising segmentation solutions using graphical statistics methodology, which can help academics and practitioners to interpret complex market segmentation solution, thus improving the practical usability of segmentation, reducing the risk of misinterpretation and contributing to closing the much-lamented "practice divide" in segmentation.
Karyotype variation of CHO host cell lines over time in culture characterized by chromosome counting and chromosome painting.
Sabine Vcelar,Vaibhav Jadhav,Michael Melcher,Norbert Auer,Astrid Hrdina,Rebecca Sagmeister,Kelley M. Heffner,Anja Puklowski,Michael J. Betenbaugh,Till Wenger,Friedrich Leisch,Martina Baumann,Nicole Borth +12 more
TL;DR: Using the population distribution of chromosome numbers per cell as well as chromosome painting to quantify the karyotypic variation in several CHO host cell lines revealed a predominant karyotype for each cell line at the start of the experiment, completed by a large number of variants present in each population.
Generalization, Combination and Extension of Functional Clustering Algorithms: The R Package funcy
TL;DR: This paper aims to show the common elements between existing models in highly cited articles, first on a theoretical basis and later their implementation is analyzed and it is illustrated how they could be improved and extended to a more general level.
NN classifiers: reducing the computational cost of cross-validation by active pattern selection
Friedrich Leisch,Lakhmi C. Jain,Kurt Hornik +2 more
- 20 Nov 1995
TL;DR: In CV/APS, the contribution of the training patterns to backpropagation learning is estimated and this information is used for active selection of CV patterns, and the computational cost of CV can be reduced to 25% of the normal costs with only small or no errors.
Identifiability of Finite Mixtures of Multinomial Logit Models with Varying and Fixed Effects
Bettina Grün,Friedrich Leisch +1 more
TL;DR: This paper analyzes the identifiability of a general class of finite mixtures of multinomial logits with varying and fixed effects, which includes the popular mult inomial logit and conditional logit models.