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Support Vector Machines (SVMs) are among the most fundamental tools for binary classification.
Estimation of Dependences Based on Empirical Data: Springer Series in Statistics (Springer Series in Statistics)
V. Vapnik · 1982
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Extensions of Lipschitz mappings into a Hilbert space
W. Johnson and J. Lindenstrauss · 1984
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A general lower bound on the number of examples needed for learning
A. Ehrenfeucht, D. Haussler, M. Kearns, and L. Valiant · 1989
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A training algorithm for optimal margin classifiers
B. E. Boser, I. M. Guyon, and V. N. Vapnik · 1992
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Support-vector networks
C. Cortes and V. Vapnik · 1995
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Boosting the margin: A new explanation for the effectiveness of voting methods
R. E. Schapire, Y. Freund, P. Bartlett, and W. S. Lee · 1998
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Generalization performance of support vector machines and other pattern classifiers
P. Bartlett and J. Shawe-Taylor · 1999
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Rademacher and gaussian complexities: Risk bounds and structural results
P. L. Bartlett and S. Mendelson · 2002
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An elementary proof of a theorem of Johnson and Lindenstrauss
S. Dasgupta and A. Gupta · 2003
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Simplified pac-bayesian margin bounds
D. A. McAllester · 2003
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Neural Network Learning: Theoretical Foundations
M. Anthony and P. L. Bartlett · 2009
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On the doubt about margin explanation of boosting
W. Gao and Z.-H. Zhou · 2013
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On the number of iterations for dantzig-wolfe optimization and packing-covering approximation algorithms
P. N. Klein and N. E. Young · 2015
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On the uniform convergence of relative frequencies of events to their probabilities
V. Vapnik and A. Chervonenkis · 2015
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Optimal compression of approximate inner products and dimension reduction
N. Alon and B. Klartag · 2017
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Tail bounds for sums of geometric and exponential variables
S. Janson · 2017
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Margin-based generalization lower bounds for boosted classifiers
A. Grønlund, L. Kamma, K. G. Larsen, A. Mathiasen, and J. Nelson · 2019
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