Global Rademacher Complexity Bounds: From Slow to Fast Convergence Rates

[1]  Davide Anguita,et al.  Unlabeled patterns to tighten Rademacher complexity error bounds for kernel classifiers , 2014, Pattern Recognit. Lett..

[2]  Marius Kloft,et al.  Learning Kernels Using Local Rademacher Complexity , 2013, NIPS.

[3]  Davide Anguita,et al.  An improved analysis of the Rademacher data-dependent bound using its self bounding property , 2013, Neural Networks.

[4]  David D. Jensen,et al.  Copy or Coincidence? A Model for Detecting Social Influence and Duplication Events , 2013, ICML.

[5]  Shie Mannor,et al.  Online Learning for Time Series Prediction , 2013, COLT.

[6]  Yiming Yang,et al.  Bayesian models for Large-scale Hierarchical Classification , 2012, NIPS.

[7]  Davide Anguita,et al.  In-Sample and Out-of-Sample Model Selection and Error Estimation for Support Vector Machines , 2012, IEEE Transactions on Neural Networks and Learning Systems.

[8]  Davide Anguita,et al.  The Impact of Unlabeled Patterns in Rademacher Complexity Theory for Kernel Classifiers , 2011, NIPS.

[9]  Davide Anguita,et al.  Maximal Discrepancy for Support Vector Machines , 2011, ESANN.

[10]  Gilles Blanchard,et al.  The Local Rademacher Complexity of Lp-Norm Multiple Kernel Learning , 2011, NIPS.

[11]  Avishek Saha,et al.  Co-regularization Based Semi-supervised Domain Adaptation , 2010, NIPS.

[12]  Ambuj Tewari,et al.  Smoothness, Low Noise and Fast Rates , 2010, NIPS.

[13]  Menno van Zaanen,et al.  Rademacher Complexity and Grammar Induction Algorithms: What It May (Not) Tell Us , 2010, ICGI.

[14]  Shiliang Sun,et al.  Sparse Semi-supervised Learning Using Conjugate Functions , 2010, J. Mach. Learn. Res..

[15]  Ohad Shamir,et al.  Learnability, Stability and Uniform Convergence , 2010, J. Mach. Learn. Res..

[16]  Xiaojin Zhu,et al.  Introduction to Semi-Supervised Learning , 2009, Synthesis Lectures on Artificial Intelligence and Machine Learning.

[17]  V. Bentkus,et al.  An extension of the Hoeffding inequality to unbounded random variables , 2008 .

[18]  Steven Abney,et al.  Semisupervised Learning for Computational Linguistics , 2007 .

[19]  Ran El-Yaniv,et al.  Transductive Rademacher Complexity and Its Applications , 2007, COLT.

[20]  A. Tsybakov,et al.  Fast learning rates for plug-in classifiers , 2007, 0708.2321.

[21]  Jean-Yves Audibert Fast learning rates in statistical inference through aggregation , 2007, math/0703854.

[22]  Peter L. Bartlett,et al.  The Rademacher Complexity of Co-Regularized Kernel Classes , 2007, AISTATS.

[23]  V. Koltchinskii Rejoinder: Local Rademacher complexities and oracle inequalities in risk minimization , 2006, 0708.0135.

[24]  Alexander Zien,et al.  Semi-Supervised Learning , 2006 .

[25]  O. Chapelle,et al.  Semi-Supervised Learning , 2006 .

[26]  Paulo J. G. Lisboa,et al.  The Use of Artificial Neural Networks in Decision Support in Cancer: a Systematic Review , 2005 .

[27]  J. Langford Tutorial on Practical Prediction Theory for Classification , 2005, J. Mach. Learn. Res..

[28]  P. Bartlett,et al.  Local Rademacher complexities , 2005, math/0508275.

[29]  Matti Kääriäinen,et al.  Generalization Error Bounds Using Unlabeled Data , 2005, COLT.

[30]  E. Rio,et al.  Concentration around the mean for maxima of empirical processes , 2005, math/0506594.

[31]  Nicu Sebe,et al.  Semisupervised learning of classifiers: theory, algorithms, and their application to human-computer interaction , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[32]  Mikhail Belkin,et al.  Regularization and Semi-supervised Learning on Large Graphs , 2004, COLT.

[33]  Mikhail Belkin,et al.  Semi-Supervised Learning on Riemannian Manifolds , 2004, Machine Learning.

[34]  T. Poggio,et al.  General conditions for predictivity in learning theory , 2004, Nature.

[35]  Peter L. Bartlett,et al.  Rademacher and Gaussian Complexities: Risk Bounds and Structural Results , 2003, J. Mach. Learn. Res..

[36]  Peter L. Bartlett,et al.  Localized Rademacher Complexities , 2002, COLT.

[37]  Vladimir Cherkassky,et al.  Model complexity control and statistical learning theory , 2002, Natural Computing.

[38]  André Elisseeff,et al.  Stability and Generalization , 2002, J. Mach. Learn. Res..

[39]  D. Panchenko Some Extensions of an Inequality of Vapnik and Chervonenkis , 2002, math/0405342.

[40]  Ming Li,et al.  Sharpening Occam's razor , 2002, Inf. Process. Lett..

[41]  Vladimir Koltchinskii,et al.  Rademacher penalties and structural risk minimization , 2001, IEEE Trans. Inf. Theory.

[42]  Robert A. Lordo,et al.  Learning from Data: Concepts, Theory, and Methods , 2001, Technometrics.

[43]  William Li,et al.  Measuring the VC-Dimension Using Optimized Experimental Design , 2000, Neural Computation.

[44]  Peter L. Bartlett,et al.  Model Selection and Error Estimation , 2000, Machine Learning.

[45]  John Langford,et al.  Computable Shell Decomposition Bounds , 2000, J. Mach. Learn. Res..

[46]  S. Boucheron,et al.  A sharp concentration inequality with applications , 1999, Random Struct. Algorithms.

[47]  E. Mammen,et al.  Smooth Discrimination Analysis , 1999 .

[48]  S. Boucheron,et al.  A sharp concentration inequality with applications , 1999, Random Struct. Algorithms.

[49]  David A. McAllester PAC-Bayesian model averaging , 1999, COLT '99.

[50]  Ayhan Demiriz,et al.  Semi-Supervised Support Vector Machines , 1998, NIPS.

[51]  John Shawe-Taylor,et al.  Structural Risk Minimization Over Data-Dependent Hierarchies , 1998, IEEE Trans. Inf. Theory.

[52]  William I. Gasarch,et al.  Book Review: An introduction to Kolmogorov Complexity and its Applications Second Edition, 1997 by Ming Li and Paul Vitanyi (Springer (Graduate Text Series)) , 1997, SIGACT News.

[53]  Gaston H. Gonnet,et al.  On the LambertW function , 1996, Adv. Comput. Math..

[54]  J. Parrondo,et al.  Vapnik-Chervonenkis bounds for generalization , 1993 .

[55]  M. Talagrand,et al.  Probability in Banach Spaces: Isoperimetry and Processes , 1991 .

[56]  Colin McDiarmid,et al.  Surveys in Combinatorics, 1989: On the method of bounded differences , 1989 .

[57]  David Haussler,et al.  Occam's Razor , 1987, Inf. Process. Lett..

[58]  E. S. Pearson,et al.  THE USE OF CONFIDENCE OR FIDUCIAL LIMITS ILLUSTRATED IN THE CASE OF THE BINOMIAL , 1934 .

[59]  Davide Anguita,et al.  Energy Efficient Smartphone-Based Activity Recognition using Fixed-Point Arithmetic , 2013, J. Univers. Comput. Sci..

[60]  Davide Anguita,et al.  A Learning Machine with a Bit-Based Hypothesis Space , 2013, ESANN.

[61]  Mikael Henaff,et al.  A comprehensive evaluation of multicategory classification methods for microbiomic data , 2013 .

[62]  Shiliang Sun,et al.  PAC-bayes bounds with data dependent priors , 2012, J. Mach. Learn. Res..

[63]  Davide Anguita,et al.  Maximal Discrepancy vs. Rademacher Complexity for error estimation , 2011, ESANN.

[64]  Shai Ben-David,et al.  Does Unlabeled Data Provably Help? Worst-case Analysis of the Sample Complexity of Semi-Supervised Learning , 2008, COLT.

[65]  Journal Url,et al.  A tail inequality for suprema of unbounded empirical processes with applications to Markov chains , 2008 .

[66]  Sally Floyd,et al.  Sample compression, learnability, and the Vapnik-Chervonenkis dimension , 2004, Machine Learning.

[67]  O. Bousquet A Bennett concentration inequality and its application to suprema of empirical processes , 2002 .

[68]  Thierry Klein Une inégalité de concentration à gauche pour les processus empiriques , 2002 .

[69]  S. Kutin Extensions to McDiarmid's inequality when dierences are bounded with high probability , 2002 .

[70]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[71]  M. Talagrand A new look at independence , 1996 .

[72]  Ming Li,et al.  An Introduction to Kolmogorov Complexity and Its Applications , 2019, Texts in Computer Science.