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dc.contributor.advisorKalyan Veeramachaneni.en_US
dc.contributor.authorWang, Alex Christopheren_US
dc.contributor.otherMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.en_US
dc.date.accessioned2016-01-15T20:47:18Z
dc.date.available2016-01-15T20:47:18Z
dc.date.copyright2015en_US
dc.date.issued2015en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/100859
dc.descriptionThesis: M. Eng. in Computer Science and Engineering, Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2015.en_US
dc.descriptionThis electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.en_US
dc.descriptionCataloged from student-submitted PDF version of thesis.en_US
dc.descriptionIncludes bibliographical references (page 71).en_US
dc.description.abstractIn this thesis, I designed, implemented, and tested a machine learning learning system designed to crowd-source feature discovery called Feature Factory. Feature Factory provides a complete web-based platform for users to define, extract, and test features on any given machine learning problem. This project involved designing, implementing, and testing a proof-of-concept version of this platform. Creating the platform involved developing user-side infrastructure and system-side infrastructure. The user-side infrastructure required careful design decisions to provide users with a clear and concise interface and workflow. The system-side infrastructure involved constructing an automated feature aggregation, extraction, and testing pipeline that can be executed with a few simple commands. Testing was performed by presenting three different machine learning problems to test users via the user-side infrastructure of Feature Factory. Users were asked to write features for the three different machine learning problems as well as comment on the usability of the system. The systemside infrastructure was utilized to analyze the effectiveness and performance of the features written by the users.en_US
dc.description.statementofresponsibilityby Alex Christopher Wang.en_US
dc.format.extent71 pagesen_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsM.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582en_US
dc.subjectElectrical Engineering and Computer Science.en_US
dc.titleFeature Factory : a collaborative, crowd-sourced machine learning systemen_US
dc.typeThesisen_US
dc.description.degreeM. Eng. in Computer Science and Engineeringen_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
dc.identifier.oclc932639544en_US


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