Zobrazeno 1 - 10
of 21
pro vyhledávání: '"Kalinke, Florian"'
Kernel methods underpin many of the most successful approaches in data science and statistics, and they allow representing probability measures as elements of a reproducing kernel Hilbert space without loss of information. Recently, the kernel Stein
Externí odkaz:
http://arxiv.org/abs/2406.08401
The correct classification of a logs assortment is crucial for the economic output within a fully mechanized timber harvest. This task is especially for unexperienced but also for professional machine operators mentally demanding. This paper presents
Externí odkaz:
https://tud.qucosa.de/id/qucosa%3A71221
https://tud.qucosa.de/api/qucosa%3A71221/attachment/ATT-0/
https://tud.qucosa.de/api/qucosa%3A71221/attachment/ATT-0/
Autor:
Kalinke, Florian, Szabo, Zoltan
Kernel techniques are among the most influential approaches in data science and statistics. Under mild conditions, the reproducing kernel Hilbert space associated to a kernel is capable of encoding the independence of $M\ge 2$ random variables. Proba
Externí odkaz:
http://arxiv.org/abs/2403.07735
In real-world applications, one often encounters ambiguously labeled data, where different annotators assign conflicting class labels. Partial-label learning allows training classifiers in this weakly supervised setting, where state-of-the-art method
Externí odkaz:
http://arxiv.org/abs/2402.00592
Autor:
Heyden, Marco, Fouché, Edouard, Arzamasov, Vadim, Fenn, Tanja, Kalinke, Florian, Böhm, Klemens
Change detection is of fundamental importance when analyzing data streams. Detecting changes both quickly and accurately enables monitoring and prediction systems to react, e.g., by issuing an alarm or by updating a learning algorithm. However, detec
Externí odkaz:
http://arxiv.org/abs/2306.12974
Autor:
Kalinke, Florian, Szabó, Zoltán
Publikováno v:
Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:1005-1015, 2023
Kernel techniques are among the most popular and powerful approaches of data science. Among the key features that make kernels ubiquitous are (i) the number of domains they have been designed for, (ii) the Hilbert structure of the function class asso
Externí odkaz:
http://arxiv.org/abs/2302.09930
Detecting changes is of fundamental importance when analyzing data streams and has many applications, e.g., in predictive maintenance, fraud detection, or medicine. A principled approach to detect changes is to compare the distributions of observatio
Externí odkaz:
http://arxiv.org/abs/2205.12706
Publikováno v:
In: Information Systems 97 (2021), p. 101705. ISSN: 0306-4379
In the real world, data streams are ubiquitous -- think of network traffic or sensor data. Mining patterns, e.g., outliers or clusters, from such data must take place in real time. This is challenging because (1) streams often have high dimensionalit
Externí odkaz:
http://arxiv.org/abs/2011.06959
Publikováno v:
In Information Systems March 2021 97
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.