{"id":{"repo_id":"mississippi","oai_identifier":"oai:egrove.olemiss.edu:etd-1943"},"canonical_url":"https://search.dev.ndltd.org/etd/mississippi/oai:egrove.olemiss.edu:etd-1943","repository":{"repo_id":"mississippi","name":"University of Mississippi","base_url":"https://egrove.olemiss.edu/do/oai/"},"display":{"title":"A Probabilistic Approach To Multiple-Instance Learning","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.","abstract_html":"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.","abstract_has_math":false,"creators":["Zhang, Silu"],"institution":null,"degree_name":"M.S. in Engineering Science","degree_level":"Thesis","degree_discipline":"Computer and Information Science","degree_department":null,"school":null,"contributors":["Yixin Chen","Xin Dang","Dawn Wilkins"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2017,"date_issued":"2017-01-01T08:00:00Z","date_published":"2017-01-01T08:00:00Z","updated_at":"2026-07-24T03:06:07Z","subjects":["Data Visualization","Multiple-Instance Learning","Non-Linear Dimensionality Reduction","Computer Sciences"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://egrove.olemiss.edu/etd/944","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Yixin Chen","Xin Dang","Dawn Wilkins"]},{"key":"dc:creator","label":"Author","values":["Zhang, Silu"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2019-06-20T07:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer and Information Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S. in Engineering Science"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Data Visualization","Multiple-Instance Learning","Non-Linear Dimensionality Reduction","Computer Sciences"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://egrove.olemiss.edu/etd/944"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["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."]},{"key":"dc:title","label":"Title","values":["A Probabilistic Approach To Multiple-Instance Learning"]}]}],"canonical_facts":{"dc:contributor":["Yixin Chen","Xin Dang","Dawn Wilkins"],"dc:creator":["Zhang, Silu"],"dc:date.available":["2019-06-20T07:00:00Z"],"dc:description.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."],"dc:identifier":["https://egrove.olemiss.edu/etd/944"],"dc:subject":["Data Visualization","Multiple-Instance Learning","Non-Linear Dimensionality Reduction","Computer Sciences"],"dc:title":["A Probabilistic Approach To Multiple-Instance Learning"],"thesis:degree_discipline":["Computer and Information Science"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S. in Engineering Science"]},"updated_at":"2026-07-24T03:06:07Z"}