Three-way Learnability: A Learning Theoretic Perspective on Three-way Decision

• Autorzy: Campagner Andrea , Ciucci Davide

Identyfikatory

DOI

10.15439/2022F18

• Języki publikacji: EN

Abstrakty

EN

In this article we study the theoretical properties of Three-way Decision (TWD) based Machine Learning, from the perspective of Computational Learning Theory, as a first attempt to bridge the gap between Machine Learning theory and Uncertainty Representation theory. Drawing on the mathematical theory of orthopairs, we provide a generalization of the PAC learning framework to the TWD setting, and we use this framework to prove a generalization of the Fundamental Theorem of Statistical Learning. We then show, by means of our main result, a connection between TWD and selective prediction.

• Wydawca: Polskie Towarzystwo Informatyczne
• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2022
• Tom: Vol. 30
• Strony: 243--246
• Opis fizyczny: Bibliogr. 26 poz.

Twórcy

autor

Campagner Andrea

autor

Ciucci Davide