David Jensen
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
David Jensen has a total of 10 co-authors. Here are the top co-authors by number of shared publications:
- Amir Houmansadr
University of Massachusetts Amherst, Amherst, MA, USA
1 shared publication - Amy McGovern
School of Meteorology University of Oklahoma Norman Oklahoma USA
1 shared publication C. Pereyda
Washington State University
1 shared publication- Jennifer Neville
Royal Holloway, University of London
1 shared publication K. Clary
University of Massachusetts Amherst
1 shared publication- Katherine Avery
College of Information and Computer Sciences, University of Massachusetts Amherst, Amherst, MA, USA
1 shared publication
Latest Publications
- Open Access
- 13 May 2024
- Published by Association for Computing Machinery (ACM)
- p. 565-568
The Effect of Alter Ego Accounts on A/B Tests in Social Networks
- 13 May 2024
- Published by Association for Computing Machinery (ACM)
- p. 565-568
Open AccessThe Effect of Alter Ego Accounts on A/B Tests in Social Networks
- Open Access
- 31 March 2005
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 170-177
Dependency Networks for Relational Data
- 31 March 2005
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 170-177
Open AccessDependency Networks for Relational Data
- 1 January 2003
- Published by Defense Technical Information Center (DTIC)
Chi-squared: A simpler evaluation function for multiple-instance learning
This paper introduces a new evaluation function for solving the multiple instance problem. Our approach makes use of the main idea of diverse density (Maron, 1998; Maron & Lozano- Perez, 1998) but finds the best concept using the chi-square statistic. This approach is simpler than diverse density and allows us to search more extensively by using properties of the contingency table to prune in a guaranteed manner. We demonstrate that this approach solves the multiple-instance problem as well as or better than diverse density and that the pruning mechanism allows chi-squared to identify the best concepts more quickly.
- 1 January 2003
- Published by Defense Technical Information Center (DTIC)
Chi-squared: A simpler evaluation function for multiple-instance learning
This paper introduces a new evaluation function for solving the multiple instance problem. Our approach makes use of the main idea of diverse density (Maron, 1998; Maron & Lozano- Perez, 1998) but finds the best concept using the chi-square statistic. This approach is simpler than diverse density and allows us to search more extensively by using properties of the contingency table to prune in a guaranteed manner. We demonstrate that this approach solves the multiple-instance problem as well as or better than diverse density and that the pruning mechanism allows chi-squared to identify the best concepts more quickly.