Date of Award
2017
Document Type
Thesis
Degree Name
M.S. in Engineering Science
Department
Computer and Information Science
First Advisor
Yixin Chen
Second Advisor
Xin Dang
Third Advisor
Dawn Wilkins
Relational Format
dissertation/thesis
Abstract
This study introduced a probabilistic approach to the multiple-instance learning (mil) problem. In particular, two bayes classication algorithms were proposed where posterior probabilities were estimated under dierent assumptions. The rst algorithm, named instance-vote, assumes that the probability of a bag being positive or negative depends upon the percentage of its instances being positive or negative. This probability is estimated using a k-nn classication of instances. In the second approach, embedded kernel density estimation (ekde), bags are represented in an instance induced (very high dimensional) space. A parametric stochastic neighbor embedding method is applied to learn a mapping that projects bags into a 2-d or 1-d space. Class conditional probability densities are then estimated in this low dimensional space via kernel density estimation. Both algorithms were evaluated using musk benchmark data sets and the results are highly competitive with existing methods.
Recommended Citation
Zhang, Silu, "A Probabilistic Approach To Multiple-Instance Learning" (2017). Electronic Theses and Dissertations. 944.
https://egrove.olemiss.edu/etd/944
Concentration/Emphasis
Emphasis: Computer Science