The Perceptron algorithm versus Winnow: Linear versus logarithmic mistake bounds when few input variables are relevant

Publikationen: Beitrag in FachzeitschriftArtikelForschung(peer-reviewed)

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The Perceptron algorithm versus Winnow: Linear versus logarithmic mistake bounds when few input variables are relevant. / Kivinen, J.; Warmuth, M. K.; Auer, P.
in: Artificial intelligence, Jahrgang 97, Nr. 1-2, 01.12.1997, S. 325-343.

Publikationen: Beitrag in FachzeitschriftArtikelForschung(peer-reviewed)

Bibtex - Download

@article{678ae357942c442286b5d4b381a41518,
title = "The Perceptron algorithm versus Winnow: Linear versus logarithmic mistake bounds when few input variables are relevant",
keywords = "Linear threshold functions, Mistake bounds, Multiplicative updates, Perceptron algorithm, Relevant variables",
author = "J. Kivinen and Warmuth, {M. K.} and P. Auer",
year = "1997",
month = dec,
day = "1",
language = "English",
volume = "97",
pages = "325--343",
journal = "Artificial intelligence",
issn = "0004-3702",
publisher = "Elsevier",
number = "1-2",

}

RIS (suitable for import to EndNote) - Download

TY - JOUR

T1 - The Perceptron algorithm versus Winnow

T2 - Linear versus logarithmic mistake bounds when few input variables are relevant

AU - Kivinen, J.

AU - Warmuth, M. K.

AU - Auer, P.

PY - 1997/12/1

Y1 - 1997/12/1

KW - Linear threshold functions

KW - Mistake bounds

KW - Multiplicative updates

KW - Perceptron algorithm

KW - Relevant variables

UR - http://www.scopus.com/inward/record.url?scp=0031375503&partnerID=8YFLogxK

M3 - Article

AN - SCOPUS:0031375503

VL - 97

SP - 325

EP - 343

JO - Artificial intelligence

JF - Artificial intelligence

SN - 0004-3702

IS - 1-2

ER -