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The support vector machine (SVM) is a well-established classification method whose name refers to the particular training examples, called support vectors, that determine the maximum margin separating hyperplane.
Geometrical and statistical properties of systems of linear inequalities with applications in pattern recognition
Thomas M Cover · 1965
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Estimation of dependences based on empirical data
Vladimir Naumovich Vapnik · 1982
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A polynomial time algorithm that learns two hidden unit nets
Eric B Baum · 1990
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A training algorithm for optimal margin classifiers
Bernhard E Boser, Isabelle M Guyon, and Vladimir N Vapnik · 1992
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Support-vector networks
Corinna Cortes and Vladimir Vapnik · 1995
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The Nature of Statistical Learning Theory
Vladimir Naumovich Vapnik · 1995
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Simplified support vector decision rules
Christopher JC Burges · 1996
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Multi-class support vector machines
Jason Weston and Chris Watkins · 1998
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Generalization performance of support vector machines and other pattern classifiers
Peter Bartlett and John Shawe-Taylor · 1999
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Statistical mechanics of support vector networks
Rainer Dietrich, Manfred Opper, and Haim Sompolinsky · 1999
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The volume of convex bodies and Banach space geometry , volume 94
Gilles Pisier · 1999
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Adaptive estimation of a quadratic functional by model selection
Beatrice Laurent and Pascal Massart · 2000
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New support vector algorithms
Bernhard Schölkopf, Alex J Smola, Robert C Williamson, and Peter L Bartlett · 2000
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Robust learning and generalization with support vector machines
Arnaud Buhot and Mirta B Gordon · 2001
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Exact simplification of support vector solutions
Tom Downs, Kevin E Gates, and Annette Masters · 2001
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Rademacher and gaussian complexities: Risk bounds and structural results
Peter L Bartlett and Shahar Mendelson · 2002
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Learning with kernels
Bernhard Schölkopf and Alexander J Smola · 2002
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Covering number bounds of certain regularized linear function classes
Tong Zhang · 2002
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Simplified PAC-Bayesian margin bounds
David McAllester · 2003
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Sparseness of support vector machines
Ingo Steinwart · 2003
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Learning intersections and thresholds of halfspaces
Adam R Klivans, Ryan O’Donnell, and Rocco A Servedio · 2004
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In defense of one-vs-all classification
Ryan Rifkin and Aldebaro Klautau · 2004
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PAC-Bayesian compression bounds on the prediction error of learning algorithms for classification
Hanson-Wright inequality and sub-gaussian concentration
Mark Rudelson and Roman Vershynin · 2013
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Asymptotics of empirical eigenstructure for high dimensional spiked covariance
Weichen Wang and Jianqing Fan · 2017
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Two models of double descent for weak features
Mikhail Belkin, Daniel Hsu, and Ji Xu · 2019
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Surprises in high-dimensional ridgeless least squares interpolation
Trevor Hastie, Andrea Montanari, Saharon Rosset, and Ryan J Tibshirani · 2019
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Exact high-dimensional asymptotics for support vector machine
Haoyang Liu · 2019
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Thore Graepel, Ralf Herbrich, and John Shawe-Taylor · 2005
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A statistical physics approach for the analysis of machine learning algorithms on real data
Dörthe Malzahn and Manfred Opper · 2005
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Classification vs regression in overparameterized regimes: Does the loss function matter?
Vidya Muthukumar, Adhyyan Narang, Vignesh Subramanian, Mikhail Belkin, Daniel Hsu, and Anant Sahai · 2005
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Building support vector machines with reduced classifier complexity
S Sathiya Keerthi, Olivier Chapelle, and Dennis DeCoste · 2006
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Sparseness vs estimating conditional probabilities: Some asymptotic results
Peter L Bartlett and Ambuj Tewari · 2007
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1-bit compressive sensing
Petros T Boufounos and Richard G Baraniuk · 2008
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Risk of the least squares minimum norm estimator under the spike covariance model
Yasaman Mahdaviyeh and Zacharie Naulet · 2019
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The generalization error of random features regression: Precise asymptotics and double descent curve
Song Mei and Andrea Montanari · 2019
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Partha P Mitra · 2019
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Benign overfitting in linear regression
Peter L. Bartlett, Philip M. Long, Gábor Lugosi, and Alexander Tsigler · 2020
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Finite-sample analysis of interpolating linear classifiers in the overparameterized regime
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Near-tight margin-based generalization bounds for support vector machines
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Just interpolate: Kernel “ridgeless” regression can generalize
Tengyuan Liang and Alexander Rakhlin · 2020
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Support vector machines and linear regression coincide with very high-dimensional features
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Risk bounds for over-parameterized maximum margin classification on sub-gaussian mixtures
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