Work

Bridging belief function theory to modern machine learning

Year: 2015

Type: preprint

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.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.1504.03874

Author: Burger, Thomas

Institutions: Centre National de la Recherche Scientifique, Inserm, Commissariat à l'Énergie Atomique et aux Énergies Alternatives, Laboratoire Biologie à Grande Échelle, Université Grenoble Alpes

Language: English

Topic: Bayesian Modeling and Causal Inference

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W1451526525

Cites: 43

Cited by:

Related to: 20