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Rademacher complexity

Known as: Rademacher 
In computational learning theory (machine learning and theory of computation), Rademacher complexity, named after Hans Rademacher, measures richness… Expand
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Papers overview

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Highly Cited
2016
Highly Cited
2016
The contraction inequality for Rademacher averages is extended to Lipschitz functions with vector-valued domains, and it is also… Expand
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Highly Cited
2010
Highly Cited
2010
Sequential algorithms of active learning based on the estimation of the level sets of the empirical risk are discussed in the… Expand
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Highly Cited
2009
Highly Cited
2009
We develop a technique for deriving data-dependent error bounds for transductive learning algorithms based on transductive… Expand
  • figure 1
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Highly Cited
2008
Highly Cited
2008
This paper presents the first Rademacher complexity-based error bounds for non-i.i.d. settings, a generalization of similar… Expand
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Highly Cited
2008
Highly Cited
2008
The Fast Johnson–Lindenstrauss Transform (FJLT) was recently discovered by Ailon and Chazelle as a novel technique for performing… Expand
  • table 1.1
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Highly Cited
2006
Highly Cited
2006
Let F be a class of measurable functions f : S → [0, 1] defined on a probability space (S, A, P). Given a sample (X 1 ,..., X n… Expand
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Highly Cited
2002
Highly Cited
2002
We investigate the use of certain data-dependent estimates of the complexity of a function class, called Rademacher and Gaussian… Expand
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Highly Cited
2001
Highly Cited
2001
We suggest a penalty function to be used in various problems of structural risk minimization. This penalty is data dependent and… Expand
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Highly Cited
2000
Highly Cited
2000
We construct data dependent upper bounds on the risk in function learning problems. The bounds are based on local norms of the… Expand
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Highly Cited
1990
Highly Cited
1990
We find upper and lower bounds for Pr(Z ?x, > t), where x1, x2 . ... are real numbers. We express the answer in terms of the K… Expand
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