The Perceptron algorithm vs. Winnow: linear vs. logarithmic mistake bounds when few input variables are relevant
Research output: Contribution to journal › Article › Research › peer-review
Standard
The Perceptron algorithm vs. Winnow: linear vs. logarithmic mistake bounds when few input variables are relevant. / Auer, Peter; Kivinen, J.; Warmuth, M. K.
In: Artificial intelligence, 1997, p. 325-343.
In: Artificial intelligence, 1997, p. 325-343.
Research output: Contribution to journal › Article › Research › peer-review
Harvard
Auer, P, Kivinen, J & Warmuth, MK 1997, 'The Perceptron algorithm vs. Winnow: linear vs. logarithmic mistake bounds when few input variables are relevant', Artificial intelligence, pp. 325-343.
APA
Auer, P., Kivinen, J., & Warmuth, M. K. (1997). The Perceptron algorithm vs. Winnow: linear vs. logarithmic mistake bounds when few input variables are relevant. Artificial intelligence, 325-343.
Vancouver
Auer P, Kivinen J, Warmuth MK. The Perceptron algorithm vs. Winnow: linear vs. logarithmic mistake bounds when few input variables are relevant. Artificial intelligence. 1997;325-343.
Author
Bibtex - Download
@article{42ed2b9fe7844967bcbf222e316bd121,
title = "The Perceptron algorithm vs. Winnow: linear vs. logarithmic mistake bounds when few input variables are relevant",
author = "Peter Auer and J. Kivinen and Warmuth, {M. K.}",
year = "1997",
language = "English",
pages = "325--343",
journal = "Artificial intelligence",
issn = "0004-3702",
publisher = "Elsevier",
}
RIS (suitable for import to EndNote) - Download
TY - JOUR
T1 - The Perceptron algorithm vs. Winnow: linear vs. logarithmic mistake bounds when few input variables are relevant
AU - Auer, Peter
AU - Kivinen, J.
AU - Warmuth, M. K.
PY - 1997
Y1 - 1997
M3 - Article
SP - 325
EP - 343
JO - Artificial intelligence
JF - Artificial intelligence
SN - 0004-3702
ER -
