Cargando…
Environmental Adaptation and Differential Replication in Machine Learning
When deployed in the wild, machine learning models are usually confronted with an environment that imposes severe constraints. As this environment evolves, so do these constraints. As a result, the feasible set of solutions for the considered need is prone to change in time. We refer to this problem...
Autores principales: | Unceta, Irene, Nin, Jordi, Pujol, Oriol |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7597251/ https://www.ncbi.nlm.nih.gov/pubmed/33286891 http://dx.doi.org/10.3390/e22101122 |
Ejemplares similares
-
Differential Replication for Credit Scoring in Regulated Environments
por: Unceta, Irene, et al.
Publicado: (2021) -
Risk mitigation in algorithmic accountability: The role of machine learning copies
por: Unceta, Irene, et al.
Publicado: (2020) -
The Challenges of Machine Learning and Their Economic Implications
por: Borrellas, Pol, et al.
Publicado: (2021) -
The limitations of machine learning models for predicting scientific replicability
por: Crockett, M. J., et al.
Publicado: (2023) -
Machine Learning-Based Surrogate Model for Press Hardening Process of 22MnB5 Sheet Steel Simulation in Industry 4.0
por: Abio, Albert, et al.
Publicado: (2022)