## Testing and learning Boolean functions

##### Author(s)

Matulef, Kevin Michael
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##### Other Contributors

Massachusetts Institute of Technology. Dept. of Mathematics.

##### Advisor

Ronitt Rubinfeld.

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Show full item record##### 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).

##### Date issued

2009##### Department

Massachusetts Institute of Technology. Department of Mathematics##### Publisher

Massachusetts Institute of Technology

##### Keywords

Mathematics.