Avrim Blum, Alan Frieze, Ravi Kannan, Santosh Vempala  Home/Search
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Abstract: In this paper we consider the problem of learning a linear threshold function (a halfspace in n dimensions, also called a "perceptron"). Methods for solving this problem generally fall into two categories. In the absence of noise, this problem can be formulated as a Linear Program and solved in polynomial time with the Ellipsoid Algorithm or Interior Point methods. Alternatively, simple greedy algorithms such as the Perceptron Algorithm are often used in practice and have certain provable... (Update)
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BibTeX entry: (Update)
A. Blum, A. Frieze, R. Kannan, and S. Vempala. A polynomial-time algorithm for learning noisy linear threshold functions. In Proc. 37th IEEE Annual Symposium on Foundations of Computer Science, 1996. http://citeseer.ist.psu.edu/blum96polynomialtime.html More
@inproceedings{ blum96polynomialtime,
author = "Avrim Blum and Alan M. Frieze and Ravi Kannan and Santosh Vempala",
title = "A Polynomial-Time Algorithm for Learning Noisy Linear Threshold Functions",
booktitle = "{IEEE} Symposium on Foundations of Computer Science",
pages = "330-338",
year = "1996",
url = "citeseer.ist.psu.edu/blum96polynomialtime.html" }
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