Zobrazeno 1 - 10
of 12
pro vyhledávání: '"Andrea Campagner"'
Publikováno v:
Theoretical Computer Science. 908:28-42
In this article, we study the application of Rough Set theory to the representation of uncertainty and partial knowledge in Dynamical Systems. Our approach draws from the abstract notion of an observable pattern, and for this purpose we first propose
In this article, we propose a general framework for the development of external evaluation measures for soft clustering. Our proposal is based on the interpretation of soft clustering as representing uncertain information about an underlying, unknown
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f6a4c461b693a4769c887ec7cd83d82e
https://hdl.handle.net/10281/401877
https://hdl.handle.net/10281/401877
Rough set theory and belief function theory, two popular mathematical frameworks for uncertainty representation, have been widely applied in different settings and contexts. Despite different origins and mathematical foundations, the fundamental conc
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::752d43a34e69ec5324b77de425dd7880
http://hdl.handle.net/10281/370582
http://hdl.handle.net/10281/370582
In this article, we study aggregation operators on shadowed sets. In particular, since shadowed sets can be obtained as approximations of fuzzy sets, we explore the relationships between aggregation operators on fuzzy sets and corresponding operators
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::160535cb8c1fcb26adc9e48c6c8a9162
http://hdl.handle.net/10281/370578
http://hdl.handle.net/10281/370578
Publikováno v:
International Journal of Approximate Reasoning. 119:292-312
In this paper, we address ambiguity, intended as a characteristic of any data expression for which a unique meaning cannot be associated by the computational agent for either lack of information or multiple interpretations of the same configuration.
This is an entry for the publication titled "Three-way decision and conformal prediction: Isomorphisms, differences and theoretical properties of cautious learning approaches". You can cite the paper as: Andrea Campagner, Federico Cabitza, Pedro Berj
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::dc0497ac842cbf7f884897ce8a27fcf1
https://zenodo.org/record/5336525
https://zenodo.org/record/5336525
Supervised learning is an important branch of machine learning (ML), which requires a complete annotation (labeling) of the involved training data. This assumption is relaxed in the settings of weakly supervised learning, where labels are allowed to
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::cbbeb8a089861667c1d56f285e5282e1
http://hdl.handle.net/10281/324845
http://hdl.handle.net/10281/324845
In recent years, Machine Learning (ML) has attracted wide interest as aid for decision makers in complex domains, such as medicine. Although domain experts are typically aware of the intrinsic uncertainty around it, the issue of Ground Truth (GT) qua
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::66d7710fba0cf65cb0946344f6d24ece
http://hdl.handle.net/10281/289890
http://hdl.handle.net/10281/289890
Publikováno v:
Information Processing and Management of Uncertainty in Knowledge-Based Systems ISBN: 9783030501457
IPMU (1)
Information Processing and Management of Uncertainty in Knowledge-Based Systems
IPMU (1)
Information Processing and Management of Uncertainty in Knowledge-Based Systems
Supervised learning is an important branch of machine learning (ML), which requires a complete annotation (labeling) of the involved training data. This assumption, which may constitute a severe bottleneck in the practical use of ML, is relaxed in we
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::059b597fe19ed1ea447d2851b6cb457f
https://doi.org/10.1007/978-3-030-50146-4_35
https://doi.org/10.1007/978-3-030-50146-4_35
We propose a method to approximate Intuitionistic Fuzzy Sets (IFSs) with Shadowed Setsthat could be used, in decision making or similar tasks, when the full information about membership values is not necessary, is difficult to process or to interpret
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::861e76ab0265ce54615394a61574e1ca
http://hdl.handle.net/10281/289888
http://hdl.handle.net/10281/289888