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Recently, there has been a renewed interest in the machine learning community for variants of a sparse greedy approximation procedure for concave optimization known as {the Frank-Wolfe (FW) method}.
An algorithm for quadratic programming
Marguerite Frank and Philip Wolfe · 1956
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An iterative procedure for computing the minimum of a quadratic form on a convex set
Elmer Gilbert · 1966
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Convergence theory in nonlinear programming
Philip Wolfe · 1970
Earlier work this paper cites.
Finding the point of a polyhedron closest to the origin
B.F. Mitchell, V.F. Dem’yanov, and V.N. Malozemov · 1974
Earlier work this paper cites.
Generalized equations and their solutions, part II: Applications to nonlinear programming
Stephen Robinson · 1982
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Some comments on Wolfe’s “away step”
Jacques Guélat and Patrice Marcotte · 1986
Earlier work this paper cites.
Nonlinear Programming: Sequential Unconstrained Minimization Techniques
Anthony V. Fiacco and Garth P. McCormick · 1990
Earlier work this paper cites.
Geometry in learning
Kristin P. Bennett and Erin J. Bredensteiner · 1997
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The kernel-adatron algorithm: A fast and simple learning procedure for support vector machines
Thilo-Thomas Friess, Nello Cristianini, and Colin Campbell · 1998
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Making large-scale support vector machine learning practical
Thorsten Joachims · 1999
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Fast training of support vector machines using sequential minimal optimization
John Platt · 1999
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Advances in kernel methods: support vector learning
Bernard Schölkopf, Christopher Burges, and Alexander Smola, editors · 1999
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Duality and geometry in SVM classifiers
Kristin P. Bennett and Erin J. Bredensteiner · 2000
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A geometric interpretation of nu-SVM classifiers
Cristopher J.C. Burges and David J. Crisp · 2000
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A fast iterative nearest point algorithm for support vector machine classifier design
S. S. Keerthi, S. K. Shevade, C. Bhattacharyya, and K. R.K. Murthy · 2000
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Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Bernard Schölkopf and Alexander Smola · 2001
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Sparse greedy matrix approximation for machine learning
Bernhard Schölkopf and Alexander J. Smola · 2001
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Efficient SVM training using low-rank kernel representations
Shai Fine and Katya Scheinberg · 2002
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A comparison of methods for multiclass support vector machines
Chih-Wei Hsu and Chih-Jen Lin · 2002
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Convergence of a generalized SMO algorithm for SVM classifier design
Sathiya Keerthi and Elmer Gilbert · 2002
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Smaller core-sets for balls
Mihai Bădoiu and Kenneth Clarkson · 2003
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Sparseness of support vector machines
Ingo Steinwart · 2003
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Sequential greedy approximation for certain convex optimization problems
Tong Zhang · 2003
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A conditional gradient method with linear rate of convergence for solving convex linear systems
Amir Beck and Marc Teboulle · 2004
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Stochastic learning
Léon Bottou · 2004
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Working set selection using second order information for training support vector machines
Rong-En Fan, Pai-Hsuen Chen, and Chih-Jen Lin · 2005
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Core vector machines: Fast SVM training on very large data sets
Ivor Tsang, James Kwok, and Pak-Ming Cheung · 2005
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Large margin methods for structured and interdependent output variables
Ioannis Tsochantaridis, Thorsten Joachims, Thomas Hofmann, and Yasemin Altun · 2005
The UCI KDD Archive. http://kdd.ics.uci.edu
Andrew Frank and Arthur Asuncion · 2010
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Fast stochastic Frank-Wolfe algorithms for nonlinear SVMs
Hua Ouyang and Alexander Gray · 2010
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An online core vector machine with adaptive MEB adjustment
Di Wang, Bo Zhang, Peng Zhang, and Hong Qiao · 2010
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LIBSVM: a library for support vector machines
Chih-Chung Chang and Chih-Jen Lin · 2011
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Training support vector machines using Frank-Wolfe methods
Emanuele Frandi, Maria Grazia Gasparo, Stefano Lodi, Ricardo Ñanculef, and Claudio Sartori · 2011
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SVM-light Support Vector Machine
Thorsten Joachims · 2011
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Statistical comparison of classifiers over multiple data sets
Janez Demsar · 2006
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Numerical optimization (2nd edition)
Jorge Nocedal and Stephen Wright · 2006
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An efficient implementation of an active set method for SVMs
Katya Scheinberg · 2006
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Generalized core vector machines
Ivor Tsang, James Kwok, and Jacek Zurada · 2006
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Predicting Structured Data (Neural Information Processing)
Gükhan Bakir, Thomas Hofmann, Bernhard Schölkopf, Alexander Smola, Ben Taskar, and S. V. N. Vishwanathan, editors · 2007
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The tradeoffs of large scale learning
Léon Bottou and Olivier Bousquet · 2007
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A linearly convergent linear-time first-order algorithm for support vector classification with a core set result
Piyush Kumar and Alper Yildirim · 2011
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Pegasos: primal estimated sub-gradient solver for SVM
Shai Shalev-Shwartz, Yoram Singer, Nathan Srebro, and Andrew Cotter · 2011
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LibCVM Toolkit. www.c2i.ntu.edu.sg/ivor/cvm.html
Ivor Tsang, Andras Kocsor, and James Kwok · 2011
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Exploiting separability in large scale linear support vector machine training
Kristian Woodsend and Jacek Gondzio · 2011
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Sublinear optimization for machine learning
Kenneth Clarkson, Elad Hazan, and David Woodruff · 2012
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Solution of classification problems via computational geometry methods
Emanuele Frandi, Maria Grazia Gasparo, Ricardo Ñanculef, and Alessandra Papini · 2012
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Projection-free online learning
Elad Hazan and Satyen Kale · 2012
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The convergence rate of the MDM algorithm
Jorge Lopez and José R Dorronsoro · 2012
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R: A Language and Environment for Statistical Computing
R Core Team · 2012
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Online learning and online convex optimization
Shai Shalev-Shwartz · 2012
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Recent advances of large-scale linear classification
Guo-Xun Yuan, Chia-Hua Ho, and Chih-Jen Lin · 2012
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Novel Frank-Wolfe methods for SVM learning
Héctor Allende, Emanuele Frandi, Ricardo Ñanculef, and Claudio Sartori · 2013
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Dan Garber and Elad Hazan · 2013
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Revisiting Frank-Wolfe: Projection-free sparse convex optimization
Martin Jaggi · 2013
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Block-coordinate Frank-Wolfe optimization for structural SVMs
Simon Lacoste-Julien, Martin Jaggi, Mark Schmidt, and Patrick Pletscher · 2013
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Stochastic dual coordinate ascent methods for regularized loss minimization
Shai Shalev-Shwartz and Tong Zhang · 2013
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