Testing and learning Boolean functions
Name
606912668-MIT.pdf
Description
Full printable version
Size
11.82 MB
Format
Adobe PDF
Checksum (MD5)
90f773fac4c05bfe706a17703fe8033c
Author(s)
Matulef, Kevin Michael
Advisor(s)
Ronitt Rubinfeld.
Date Issued
2009
Publisher
Massachusetts Institute of Technology
Abstract
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.
Description
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mathematics, 2009.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 203-207).
Subjects
Mathematics.
MIT Department
Massachusetts Institute of Technology. Department of Mathematics
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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.
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