Cargando…
A Formal Framework for Knowledge Acquisition: Going beyond Machine Learning
Philosophers frequently define knowledge as justified, true belief. We built a mathematical framework that makes it possible to define learning (increasing number of true beliefs) and knowledge of an agent in precise ways, by phrasing belief in terms of epistemic probabilities, defined from Bayes’ r...
Autores principales: | Hössjer, Ola, Díaz-Pachón, Daniel Andrés, Rao, J. Sunil |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9601974/ https://www.ncbi.nlm.nih.gov/pubmed/37420489 http://dx.doi.org/10.3390/e24101469 |
Ejemplares similares
-
Assessing, Testing and Estimating the Amount of Fine-Tuning by Means of Active Information
por: Díaz-Pachón, Daniel Andrés, et al.
Publicado: (2022) -
An ontological framework for the formalization, organization and usage of TCM-Knowledge
por: Long, Hai, et al.
Publicado: (2019) -
Avoid Oversimplifications in Machine Learning: Going beyond the Class-Prediction Accuracy
por: Ho, Sung Yang, et al.
Publicado: (2020) -
A simple correction for COVID-19 sampling bias
por: Díaz–Pachón, Daniel Andrés, et al.
Publicado: (2020) -
A simple correction for COVID-19 sampling bias
por: Díaz-Pachón, Daniel Andrés, et al.
Publicado: (2021)