2026-09-18T20:36:54Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1217422021-07-05T14:03:20Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Tamara Broderick.
Stephenson, William T.(William Thomas)
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2019-07-17T20:59:47Z
2019-07-17T20:59:47Z
2019
2019
https://hdl.handle.net/1721.1/121742
1102051192
Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 59-60).
Cross validation (CV) is an effective yet computationally expensive tool for assessing the out of sample error for many methods in machine learning and statistics. Previous work has shown that methods to approximate CV can be very accurate and computationally cheap, but only for low dimensional problems. In this thesis, a modification of existing methods is developed to extend the high accuracy of these techniques to high dimensional settings.
by William T. Stephenson.
S.M.
S.M. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science
60 pages
eng
Massachusetts Institute of Technology
MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.
http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
Approximate cross validation for sparse generalized linear models
Thesis
application/pdf
2019-07-17T20:59:44Z
Publication
Master
EECS
unknown
unknown
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