Zobrazeno 1 - 10
of 6 804
pro vyhledávání: '"Schollmeyer, P"'
We demonstrate that a wide array of machine learning algorithms are specific instances of one single paradigm: reciprocal learning. These instances range from active learning over multi-armed bandits to self-training. We show that all these algorithm
Externí odkaz:
http://arxiv.org/abs/2408.06257
Given the vast number of classifiers that have been (and continue to be) proposed, reliable methods for comparing them are becoming increasingly important. The desire for reliability is broken down into three main aspects: (1) Comparisons should allo
Externí odkaz:
http://arxiv.org/abs/2406.03924
Autor:
Blocher, Hannah, Schollmeyer, Georg
In this article we introduce a notion of depth functions for data types that are not given in standard statistical data formats. We focus on data that cannot be represented by one specific data structure, such as normed vector spaces. This covers a w
Externí odkaz:
http://arxiv.org/abs/2402.16560
We propose a framework for descriptively analyzing sets of partial orders based on the concept of depth functions. Despite intensive studies in linear and metric spaces, there is very little discussion on depth functions for non-standard data types s
Externí odkaz:
http://arxiv.org/abs/2312.12839
Spaces with locally varying scale of measurement, like multidimensional structures with differently scaled dimensions, are pretty common in statistics and machine learning. Nevertheless, it is still understood as an open question how to exploit the e
Externí odkaz:
http://arxiv.org/abs/2306.12803
We propose a framework for descriptively analyzing sets of partial orders based on the concept of depth functions. Despite intensive studies of depth functions in linear and metric spaces, there is very little discussion on depth functions for non-st
Externí odkaz:
http://arxiv.org/abs/2304.09872
Autor:
Schollmeyer, Georg, Blocher, Hannah
This short note describes and proves a connectedness property which was introduced in Blocher et al. [2023] in the context of data depth functions for partial orders. The connectedness property gives a structural insight into union-free generic sets.
Externí odkaz:
http://arxiv.org/abs/2304.10549
Self-training is a simple yet effective method within semi-supervised learning. The idea is to iteratively enhance training data by adding pseudo-labeled data. Its generalization performance heavily depends on the selection of these pseudo-labeled da
Externí odkaz:
http://arxiv.org/abs/2303.01117
The quality of consequences in a decision making problem under (severe) uncertainty must often be compared among different targets (goals, objectives) simultaneously. In addition, the evaluations of a consequence's performance under the various targe
Externí odkaz:
http://arxiv.org/abs/2212.06832
Publikováno v:
IUCrData, Vol 9, Iss 10, p x241032 (2024)
The title compound, C14H20NO5+·Cl−, was prepared as a racemate of R,R- and S,S-enantiomers by reduction of the corresponding hydroxyiminoketone. In the crystal, layers are formed via hydrogen bridges of four ammonium groups to chloride ions; these
Externí odkaz:
https://doaj.org/article/57bdbde4cb5d40229ada95717a1ec32a
