{"id":{"repo_id":"wfu","oai_identifier":"oai:wakespace.lib.wfu.edu:10339/59315"},"canonical_url":"https://search.dev.ndltd.org/etd/wfu/oai:wakespace.lib.wfu.edu:10339/59315","repository":{"repo_id":"wfu","name":"Wake Forest University","base_url":"https://wakespace.lib.wfu.edu/oai/request"},"display":{"title":"Performance Analysis of Parallel Support Vector Machines on a MapReduce Architecture","abstract":"The quantity of electronic data available for analysis has grown exponentially with the rapid development of the World Wide Web, the Internet of Things, and other digital technologies. As a result, data mining and machine learning algorithms face computational complexity issues when applied to real world datasets. Support Vector Machines (SVM) are powerful classification and regression tools but their computational requirements increase rapidly as the number of training examples increases. To address this problem, several parallel MapReduce based implementations of SVMs have been proposed. These implementation have in common that they decompose a large-scale multi-class problem to a number of relatively smaller subproblems by dividing the data into multiple partitions which can be processed in parallel; however, these approaches use different aggregation and combination strategies to form the final model.","abstract_html":"The quantity of electronic data available for analysis has grown exponentially with the rapid development of the World Wide Web, the Internet of Things, and other digital technologies. As a result, data mining and machine learning algorithms face computational complexity issues when applied to real world datasets. Support Vector Machines (SVM) are powerful classification and regression tools but their computational requirements increase rapidly as the number of training examples increases. To address this problem, several parallel MapReduce based implementations of SVMs have been proposed. These implementation have in common that they decompose a large-scale multi-class problem to a number of relatively smaller subproblems by dividing the data into multiple partitions which can be processed in parallel; however, these approaches use different aggregation and combination strategies to form the final model.","abstract_has_math":false,"creators":["Patel, Udita"],"institution":"Wake Forest University","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2016,"date_issued":"2016","date_published":"2016","updated_at":"2026-07-27T22:02:05Z","subjects":["Machine Learning"],"languages":["en"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/10339/59315","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Patel, Udita"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2016-05-21T08:35:51Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2018-05-20T08:30:10Z"]},{"key":"dc:date.issued","label":"Date","values":["2016"]},{"key":"dc:publisher","label":"Institution","values":["Wake Forest University"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Machine Learning"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/10339/59315"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["The quantity of electronic data available for analysis has grown exponentially with the rapid development of the World Wide Web, the Internet of Things, and other digital technologies. As a result, data mining and machine learning algorithms face computational complexity issues when applied to real world datasets. Support Vector Machines (SVM) are powerful classification and regression tools but their computational requirements increase rapidly as the number of training examples increases. To address this problem, several parallel MapReduce based implementations of SVMs have been proposed. These implementation have in common that they decompose a large-scale multi-class problem to a number of relatively smaller subproblems by dividing the data into multiple partitions which can be processed in parallel; however, these approaches use different aggregation and combination strategies to form the final model."]},{"key":"dc:title","label":"Title","values":["Performance Analysis of Parallel Support Vector Machines on a MapReduce Architecture"]}]}],"canonical_facts":{"dc:creator":["Patel, Udita"],"dc:date.accessioned":["2016-05-21T08:35:51Z"],"dc:date.available":["2018-05-20T08:30:10Z"],"dc:date.issued":["2016"],"dc:description.abstract":["The quantity of electronic data available for analysis has grown exponentially with the rapid development of the World Wide Web, the Internet of Things, and other digital technologies. As a result, data mining and machine learning algorithms face computational complexity issues when applied to real world datasets. Support Vector Machines (SVM) are powerful classification and regression tools but their computational requirements increase rapidly as the number of training examples increases. To address this problem, several parallel MapReduce based implementations of SVMs have been proposed. These implementation have in common that they decompose a large-scale multi-class problem to a number of relatively smaller subproblems by dividing the data into multiple partitions which can be processed in parallel; however, these approaches use different aggregation and combination strategies to form the final model."],"dc:identifier.uri":["http://hdl.handle.net/10339/59315"],"dc:language.iso":["en"],"dc:publisher":["Wake Forest University"],"dc:subject":["Machine Learning"],"dc:title":["Performance Analysis of Parallel Support Vector Machines on a MapReduce Architecture"],"dc:type":["Thesis"]},"updated_at":"2026-07-27T22:02:05Z"}