Electronic Theses and Dissertations

Date of Award


Document Type


Degree Name

M.S. in Engineering Science


Computer and Information Science

First Advisor

Yixin Chen

Second Advisor

Xin Dang

Third Advisor

Dawn Wilkins

Relational Format



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.


Emphasis: Computer Science



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