Work

Distribution Kernel Methods for Multiple-Instance Learning

Year: 2013

Type: conference-paper

Abstract: I propose to investigate learning in the multiple-instance (MI) framework as a problem of learning from distributions. In many MI applications, bags of instances can be thought of as samples from bag-generating distributions. Recent kernel approaches for learning from distributions have the potential to be successfully applied to these domains and other MI learning problems. Understanding when distribution-based techniques work for MI learning will lead to new theoretical insights, improved algorithms, and more accurate solutions for real-world problems.

Source: Proceedings of the AAAI Conference on Artificial Intelligence

DOI: 10.1609/aaai.v27i1.8501

Author: Gary Doran

Institution: Case Western Reserve University

Language: English

Topic: Machine Learning and Algorithms

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: diamond

OpenAlex ID: W2189483767

FWCI:

Cites: 10

Cited by: 3

Related to: 10