Zobrazeno 1 - 10
of 347
pro vyhledávání: '"Schmid, Jochen"'
Informed learning is an emerging field in machine learning that aims to compensate for insufficient data with prior knowledge. Shape knowledge covers many types of prior knowledge concerning the relationship of a function's output with respect to inp
Externí odkaz:
http://arxiv.org/abs/2409.17084
Autor:
Bubel, Martin, Schmid, Jochen, Carmesin, Maximilian, Kozachynskyi, Volodymyr, Esche, Erik, Bortz, Michael
Calibrating model parameters to measured data by minimizing loss functions is an important step in obtaining realistic predictions from model-based approaches, e.g., for process optimization. This is applicable to both knowledge-driven and data-drive
Externí odkaz:
http://arxiv.org/abs/2409.08756
We develop adaptive discretization algorithms for locally optimal experimental design of nonlinear prediction models. With these algorithms, we refine and improve a pertinent state-of-the-art algorithm in various respects. We establish novel terminat
Externí odkaz:
http://arxiv.org/abs/2406.01541
We propose a general methodology of sequential locally optimal design of experiments for explicit or implicit nonlinear models, as they abound in chemical engineering and, in particular, in vapor-liquid equilibrium modeling. As a sequential design me
Externí odkaz:
http://arxiv.org/abs/2403.09443
Autor:
Link, Patrick, Poursanidis, Miltiadis, Schmid, Jochen, Zache, Rebekka, von Kurnatowski, Martin, Teicher, Uwe, Ihlenfeldt, Steffen
Increasing digitalization enables the use of machine learning methods for analyzing and optimizing manufacturing processes. A main application of machine learning is the construction of quality prediction models, which can be used, among other things
Externí odkaz:
http://arxiv.org/abs/2202.02003
Autor:
Schilling, Julia, Schmid, Jochen
Publikováno v:
In New BIOTECHNOLOGY 25 September 2024 82:75-84
Autor:
Schmid, Jochen, Poursanidis, Miltiadis
We develop two adaptive discretization algorithms for convex semi-infinite optimization, which terminate after finitely many iterations at approximate solutions of arbitrary precision. In particular, they terminate at a feasible point of the consider
Externí odkaz:
http://arxiv.org/abs/2105.08417
Publikováno v:
PLOS ONE 18(1): e0279876 (2023)
We propose a novel methodology for general multi-class classification in arbitrary feature spaces, which results in a potentially well-calibrated classifier. Calibrated classifiers are important in many applications because, in addition to the predic
Externí odkaz:
http://arxiv.org/abs/2103.02926