mistake bounds
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Mistake bounds on the noise-free multi-armed bandit game

Information and Computation ◽

10.1016/j.ic.2019.104453 ◽

2019 ◽

Vol 269 ◽

pp. 104453 ◽

Cited By ~ 1

Author(s):

Atsuyoshi Nakamura ◽

David P. Helmbold ◽

Manfred K. Warmuth

Keyword(s):

Mistake Bounds

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Corrigendum to “Shifting: One-inclusion mistake bounds and sample compression” [J. Comput. System Sci. 75 (1) (2009) 37–59]

Journal of Computer and System Sciences ◽

10.1016/j.jcss.2010.03.001 ◽

2010 ◽

Vol 76 (3-4) ◽

pp. 278-280

Author(s):

Benjamin I.P. Rubinstein ◽

Peter L. Bartlett ◽

J. Hyam Rubinstein

Keyword(s):

Mistake Bounds ◽

Sample Compression

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Online Multiple Kernel Learning: Algorithms and Mistake Bounds

Lecture Notes in Computer Science - Algorithmic Learning Theory ◽

10.1007/978-3-642-16108-7_31 ◽

2010 ◽

pp. 390-404 ◽

Cited By ~ 24

Author(s):

Rong Jin ◽

Steven C. H. Hoi ◽

Tianbao Yang

Keyword(s):

Learning Algorithms ◽

Multiple Kernel Learning ◽

Kernel Learning ◽

Mistake Bounds ◽

Multiple Kernel

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Shifting: One-inclusion mistake bounds and sample compression

Journal of Computer and System Sciences ◽

10.1016/j.jcss.2008.07.005 ◽

2009 ◽

Vol 75 (1) ◽

pp. 37-59 ◽

Cited By ~ 11

Author(s):

Benjamin I.P. Rubinstein ◽

Peter L. Bartlett ◽

J. Hyam Rubinstein

Keyword(s):

Mistake Bounds ◽

Sample Compression

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Shifting, One-Inclusion Mistake Bounds and Tight Multiclass Expected Risk Bounds

Advances in Neural Information Processing Systems 19 ◽

10.7551/mitpress/7503.003.0154 ◽

2007 ◽

Keyword(s):

Mistake Bounds ◽

Risk Bounds

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On a Generalized Notion of Mistake Bounds

Information and Computation ◽

10.1006/inco.2000.3001 ◽

2001 ◽

Vol 166 (2) ◽

pp. 156-166 ◽

Cited By ~ 1

Author(s):

Sanjay Jain ◽

Arun Sharma

Keyword(s):

Mistake Bounds

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On a generalized notion of mistake bounds

Proceedings of the twelfth annual conference on Computational learning theory - COLT '99 ◽

10.1145/307400.307450 ◽

1999 ◽

Author(s):

Sanjay Jain ◽

Arun Sharma

Keyword(s):

Mistake Bounds

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The perceptron algorithm versus winnow: linear versus logarithmic mistake bounds when few input variables are relevant

Artificial Intelligence ◽

10.1016/s0004-3702(97)00039-8 ◽

1997 ◽

Vol 97 (1-2) ◽

pp. 325-343 ◽

Cited By ~ 33

Author(s):

J. Kivinen ◽

M.K. Warmuth ◽

P. Auer

Keyword(s):

Perceptron Algorithm ◽

Mistake Bounds ◽

Input Variables

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mistake boundsRecently Published Documents

TOTAL DOCUMENTS

H-INDEX

Mistake bounds on the noise-free multi-armed bandit game

Corrigendum to “Shifting: One-inclusion mistake bounds and sample compression” [J. Comput. System Sci. 75 (1) (2009) 37–59]

Online Multiple Kernel Learning: Algorithms and Mistake Bounds

Shifting: One-inclusion mistake bounds and sample compression

Shifting, One-Inclusion Mistake Bounds and Tight Multiclass Expected Risk Bounds

On a Generalized Notion of Mistake Bounds

On a generalized notion of mistake bounds

The perceptron algorithm versus winnow: linear versus logarithmic mistake bounds when few input variables are relevant

mistake bounds
Recently Published Documents