Fast convergence of extended Rademacher Complexity bounds

No abstract is available for this paper.

• Publication year

  2015

• Venue

  IEEE International Joint Conference on Neural Network

• Publication date

  2015-07-12

• Fields of study

  Mathematics, Computer Science

• Identifiers
  DOI 10.1109/IJCNN.2015.7280414
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

• Statistical Learning Theory

  2021influential reference
• Semi-Supervised Learning

  2019cited by this paper
• Unlabeled patterns to tighten Rademacher complexity error bounds for kernel classifiers

  2014cited by this paper
• A Learning Machine with a Bit-Based Hypothesis Space

  2013cited by this paper
• An improved analysis of the Rademacher data-dependent bound using its self bounding property

  2013influential reference
• In-Sample and Out-of-Sample Model Selection and Error Estimation for Support Vector Machines

  2012influential reference
• PAC-bayes bounds with data dependent priors

  2012cited by this paper
• In-sample model selection for Support Vector Machines

  2011cited by this paper
• Maximal Discrepancy vs. Rademacher Complexity for error estimation

  2011cited by this paper
• Smoothness, Low Noise and Fast Rates

  2010cited by this paper
• Learning and Smoothed Analysis

  2009cited by this paper
• A survey of cross-validation procedures for model selection

  2009cited by this paper
• Fast learning rates in statistical inference through aggregation

  2007cited by this paper
• Book Reviews: Semisupervised Learning for Computational Linguistics by Steven Abney

  2007cited by this paper
• Trading convexity for scalability

  2006cited by this paper
• Local Rademacher complexities and oracle inequalities in risk minimization

  2006cited by this paper
• Rejoinder: Local Rademacher complexities and oracle inequalities in risk minimization

  2006cited by this paper
• Fast learning rates for plug-in classifiers

  2005cited by this paper
• Tutorial on Practical Prediction Theory for Classification

  2005cited by this paper
• Concentration around the mean for maxima of empirical processes

  2005cited by this paper
• Local Rademacher complexities

  2005cited by this paper
• Semi-Supervised Learning on Riemannian Manifolds

  2004cited by this paper
• Semisupervised learning of classifiers: theory, algorithms, and their application to human-computer interaction

  2004cited by this paper
• General conditions for predictivity in learning theory

  2004cited by this paper
• Regularization and Semi-supervised Learning on Large Graphs

  2004cited by this paper
• Rademacher and Gaussian Complexities: Risk Bounds and Structural Results

  2003cited by this paper
• A Bennett concentration inequality and its application to suprema of empirical processes

  2002cited by this paper
• Une inégalité de concentration à gauche pour les processus empiriques

  2002cited by this paper
• Localized Rademacher Complexities

  2002cited by this paper
• Stability and Generalization

  2002cited by this paper
• An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods

  2001cited by this paper
• Rademacher penalties and structural risk minimization

  2001cited by this paper
• Computable Shell Decomposition Bounds

  2000cited by this paper
• A sharp concentration inequality with applications

  1999cited by this paper
• PAC-Bayesian model averaging

  1999cited by this paper
• Smooth Discrimination Analysis

  1999cited by this paper
• Semi-Supervised Support Vector Machines

  1998cited by this paper
• On the LambertW function

  1996cited by this paper
• A new look at independence

  1996influential reference
• Sample compression, learnability, and the Vapnik-Chervonenkis dimension

  1995cited by this paper
• Probability in Banach Spaces: Isoperimetry and Processes

  1991cited by this paper
• Surveys in Combinatorics, 1989: On the method of bounded differences

  1989influential reference
• The jackknife, the bootstrap, and other resampling plans

  1987cited by this paper
• A pattern recognition approach to understanding the multi-layer perception

  1987cited by this paper
• Occam's Razor

  1987cited by this paper
• WEIGHTED SUMS OF CERTAIN DEPENDENT RANDOM VARIABLES

  1967cited by this paper
• THE USE OF CONFIDENCE OR FIDUCIAL LIMITS ILLUSTRATED IN THE CASE OF THE BINOMIAL

  1934cited by this paper

• ESTIMATION OF MINIMAL INITIAL SAMPLE SIZE IN PROGRESSIVE SAMPLING FOR BIG DATA ANALYTICS

  year unknowncites this paper