An improved multiple-instance learning algorithm

3Citations
Citations of this article
1Readers
Mendeley users who have this article in their library.
Get full text

Abstract

Multiple-instance learning (MIL) is a variation on supervised learning, where the task is to learn a concept given positive and negative bags of instances. In this paper a novel algorithm has been introduced for multiple-instance learning. This method was inspired by both diverse density (DD) and its expectation maximization version (EM-DD). It converts MIL problem to a single-instance setting. This improved method has better accuracy and time complexity than DD and EM-DD. We apply it to drug activity prediction and image retrieval. The experiments show it has competitive accuracy values compared with other previous approaches. © Springer-Verlag Berlin Heidelberg 2007.

Cite

CITATION STYLE

APA

Han, F., Wang, D., & Liao, X. (2007). An improved multiple-instance learning algorithm. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4491 LNCS, pp. 1104–1109). Springer Verlag. https://doi.org/10.1007/978-3-540-72383-7_129

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free