Bridging belief function theory to modern machine learning

TL;DR

This paper explores how belief function theory can be integrated into modern machine learning to address new challenges and trends in the rapidly evolving field.

Contribution

It highlights the potential role of belief function theory in advancing modern machine learning methodologies.

Findings

Belief function theory can enhance uncertainty modeling in machine learning.

The paper identifies key trends and questions in modern machine learning.

Belief functions offer a promising framework for future research in AI.

Abstract

Machine learning is a quickly evolving field which now looks really different from what it was 15 years ago, when classification and clustering were major issues. This document proposes several trends to explore the new questions of modern machine learning, with the strong afterthought that the belief function framework has a major role to play.