Susanne Saminger-Platz
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Previous Organizations / Institutes
The organizations / institutes from the scholar's other articles in the past ten years
Susanne Saminger-Platz has a total of 72 co-authors. Here are the top co-authors by number of shared publications:
- Erich Peter Klement
Johannes Kepler University, Linz, Austria
15 shared publications - Radko Mesiar
Faculty of Civil Engineering, Slovak University of Technology, Bratislava, Slovakia
9 shared publications - Adriana Kolesarova
AgroBioTech Research Centre, Slovak University of Agriculture in Nitra, Tr. A. Hlinku 2, 949 76 Nitra, Slovakia
6 shared publications - Bernard De Baets
KERMIT, Department of Data Analysis and Mathematical Modelling, Ghent University, Gent, Belgium
6 shared publications - Ulrich Bodenhofer
School of Informatics, Communications, and Media, University of Applied Sciences Upper Austria, Hagenberg, Austria
6 shared publications - Alexandru-Ciprian Zăvoianu
Robert Gordon University
5 shared publications
Latest Publications
- Open Access
Online Regularized Learning Algorithms in RKHS with $β$- and $φ$-Mixing Sequences
In this paper, we study an online regularized learning algorithm in a reproducing kernel Hilbert spaces (RKHS) based on a class of dependent processes. We choose such a process where the degree of dependence is measured by mixing coefficients. As a representative example, we analyze a strictly stationary Markov chain, where the dependence structure is characterized by the \(φ\)- and \(β\)-mixing coefficients. Under these assumptions, we derive probabilistic upper bounds as well as convergence rates for both the exponential and polynomial decay of the mixing coefficients.Open AccessOnline Regularized Learning Algorithms in RKHS with $β$- and $φ$-Mixing Sequences
In this paper, we study an online regularized learning algorithm in a reproducing kernel Hilbert spaces (RKHS) based on a class of dependent processes. We choose such a process where the degree of dependence is measured by mixing coefficients. As a representative example, we analyze a strictly stationary Markov chain, where the dependence structure is characterized by the \(φ\)- and \(β\)-mixing coefficients. Under these assumptions, we derive probabilistic upper bounds as well as convergence rates for both the exponential and polynomial decay of the mixing coefficients. - 10 August 2024
- Published by Springer Nature
- p. 413-420
Learning in Reproducing Kernel Hilbert Spaces for Orbits of Iterated Function Systems
- 10 August 2024
- Published by Springer Nature
- p. 413-420
Learning in Reproducing Kernel Hilbert Spaces for Orbits of Iterated Function Systems
- 10 August 2024
- Published by Springer Nature
- p. 429-439
Copulas and Triangular Norms: Selected Commonalities and Differences
- 10 August 2024
- Published by Springer Nature
- p. 429-439
Copulas and Triangular Norms: Selected Commonalities and Differences
- Open Access
- 1 January 2024
- Published by Walter de Gruyter GmbH in Dependence Modeling
- Vol. 12 (1)
Special Issue: 40th Linz Seminar on Fuzzy Set Theory. Copulas – Theory and Applications
Article Special Issue: 40th Linz Seminar on Fuzzy Set Theory. Copulas – Theory and Applications was published on January 1, 2024 in the journal Dependence Modeling (volume 12, issue 1).
- 1 January 2024
- Published by Walter de Gruyter GmbH in Dependence Modeling
- Vol. 12 (1)
Open AccessSpecial Issue: 40th Linz Seminar on Fuzzy Set Theory. Copulas – Theory and Applications
Article Special Issue: 40th Linz Seminar on Fuzzy Set Theory. Copulas – Theory and Applications was published on January 1, 2024 in the journal Dependence Modeling (volume 12, issue 1).
- Open Access
- 1 January 2024
- Published by Walter de Gruyter GmbH in Dependence Modeling
- Vol. 12 (1)
On comprehensive families of copulas involving the three basic copulas and transformations thereof
Comprehensive families of copulas including the three basic copulas (at least as limit cases) are useful tools to model countermonotonicity, independence, and comonotonicity of pairs of random variables on the same probability space. In this contribution, we study how the transition from a (basic) copula to a copula modeling a different dependence behavior can be realized by means of ordinal sums based on one of the three basic copulas, perturbing one of the three basic copulas (considering some appropriate parameterized transformations) and truncating the results using the Fréchet-Hoeffding bounds. We provide results and examples showing the flexibility and the restrictions for obtaining new copulas or comprehensive families and illustrate the development of their dependence parameters.
- 1 January 2024
- Published by Walter de Gruyter GmbH in Dependence Modeling
- Vol. 12 (1)
Open AccessOn comprehensive families of copulas involving the three basic copulas and transformations thereof
Comprehensive families of copulas including the three basic copulas (at least as limit cases) are useful tools to model countermonotonicity, independence, and comonotonicity of pairs of random variables on the same probability space. In this contribution, we study how the transition from a (basic) copula to a copula modeling a different dependence behavior can be realized by means of ordinal sums based on one of the three basic copulas, perturbing one of the three basic copulas (considering some appropriate parameterized transformations) and truncating the results using the Fréchet-Hoeffding bounds. We provide results and examples showing the flexibility and the restrictions for obtaining new copulas or comprehensive families and illustrate the development of their dependence parameters.
- Open Access
Coherence and avoidance of sure loss for standardized functions and semicopulas
We discuss avoidance of sure loss and coherence results for semicopulas and standardized functions, i.e., for grounded, 1-increasing functions with value $1$ at $(1,1,\ldots, 1)$. We characterize the existence of a $k$-increasing $n$-variate function $C$ fulfilling $A\leq C\leq B$ for standardized $n$-variate functions $A,B$ and discuss the method for constructing this function. Our proofs also include procedures for extending functions on some countably infinite mesh to functions on the unit box. We provide a characterization when $A$ respectively $B$ coincides with the pointwise infimum respectively supremum of the set of all $k$-increasing $n$-variate functions $C$ fulfilling $A\leq C\leq B$.Open AccessCoherence and avoidance of sure loss for standardized functions and semicopulas
We discuss avoidance of sure loss and coherence results for semicopulas and standardized functions, i.e., for grounded, 1-increasing functions with value $1$ at $(1,1,\ldots, 1)$. We characterize the existence of a $k$-increasing $n$-variate function $C$ fulfilling $A\leq C\leq B$ for standardized $n$-variate functions $A,B$ and discuss the method for constructing this function. Our proofs also include procedures for extending functions on some countably infinite mesh to functions on the unit box. We provide a characterization when $A$ respectively $B$ coincides with the pointwise infimum respectively supremum of the set of all $k$-increasing $n$-variate functions $C$ fulfilling $A\leq C\leq B$. - Open Access
- 19 September 2023
- Published by Springer Nature in Evolving Systems
- Vol. 14 (5) , 747-748
Obituary Edwin Lughofer (1972–2023)
- 19 September 2023
- Published by Springer Nature in Evolving Systems
- Vol. 14 (5) , 747-748
Open AccessObituary Edwin Lughofer (1972–2023)
- Open Access
- 30 October 2022
- Published by Springer Nature in Statistical Papers
- Vol. 64 (6) , 2015-2055
Multivariate copulas with given values at two arbitrary points
Copulas are functions that link an n-dimensional distribution function with its one-dimensional margins. In this contribution we show how n-variate copulas with given values at two arbitrary points can be constructed. Thereby, we also answer a so far open question whether lower and upper bounds for n-variate copulas with given value at a single arbitrary point are achieved. We also introduce and discuss the concept of an $$\mathbf{F}$$ -copula which is needed for proving our results.- 30 October 2022
- Published by Springer Nature in Statistical Papers
- Vol. 64 (6) , 2015-2055
Open AccessMultivariate copulas with given values at two arbitrary points
Copulas are functions that link an n-dimensional distribution function with its one-dimensional margins. In this contribution we show how n-variate copulas with given values at two arbitrary points can be constructed. Thereby, we also answer a so far open question whether lower and upper bounds for n-variate copulas with given value at a single arbitrary point are achieved. We also introduce and discuss the concept of an $$\mathbf{F}$$ -copula which is needed for proving our results. - Open Access
- 22 April 2022
- Published by Elsevier in Fuzzy Sets and Systems
- Vol. 451, 28-64
Ordinal sums: From triangular norms to bi- and multivariate copulas
- 22 April 2022
- Published by Elsevier in Fuzzy Sets and Systems
- Vol. 451, 28-64
Open AccessOrdinal sums: From triangular norms to bi- and multivariate copulas
- Open Access
On generalization in moment-based domain adaptation
Domain adaptation algorithms are designed to minimize the misclassification risk of a discriminative model for a target domain with little training data by adapting a model from a source domain with a large amount of training data. Standard approaches measure the adaptation discrepancy based on distance measures between the empirical probability distributions in the source and target domain. In this setting, we address the problem of deriving generalization bounds under practice-oriented general conditions on the underlying probability distributions. As a result, we obtain generalization bounds for domain adaptation based on finitely many moments and smoothness conditions.
Open AccessOn generalization in moment-based domain adaptation
Domain adaptation algorithms are designed to minimize the misclassification risk of a discriminative model for a target domain with little training data by adapting a model from a source domain with a large amount of training data. Standard approaches measure the adaptation discrepancy based on distance measures between the empirical probability distributions in the source and target domain. In this setting, we address the problem of deriving generalization bounds under practice-oriented general conditions on the underlying probability distributions. As a result, we obtain generalization bounds for domain adaptation based on finitely many moments and smoothness conditions.