2026-09-19T14:23:57Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/546632022-01-13T07:54:35Zcom_1721.1_7582com_1721.1_7581col_1721.1_131022
Ronitt Rubinfeld.
Matulef, Kevin Michael
Massachusetts Institute of Technology. Dept. of Mathematics.
Massachusetts Institute of Technology. Department of Mathematics
2010-04-28T17:16:48Z
2010-04-28T17:16:48Z
2009
2009
http://hdl.handle.net/1721.1/54663
606912668
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mathematics, 2009.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 203-207).
Given a function f on n inputs, we consider the problem of testing whether f belongs to a concept class C, or is far from every member of C. An algorithm that achieves this goal for a particular C is called a property testing algorithm, and can be viewed as relaxation of a proper learning algorithm, which must also return an approximation to f if it is in C. We give property testing algorithms for many different classes C, with a focus on those that are fundamental to machine learning, such as halfspaces, decision trees, DNF formulas, and sparse polynomials. In almost all cases, the property testing algorithm has query complexity independent of n, better than the best possible learning algorithm.
by Kevin Michael Matulef.
Ph.D.
207 p.
eng
Massachusetts Institute of Technology
M.I.T. theses are protected by
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http://dspace.mit.edu/handle/1721.1/7582
Mathematics.
Testing and learning Boolean functions
Thesis
application/pdf
Publication
unknown
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