Fetching the paper…
Reading the bibliography…
We investigate the relation of two fundamental tools in machine learning and signal processing, that is the support vector machine (SVM) for classification, and the Lasso technique used in regression.
The Perceptron: A Probabilistic Model for Information Storage and Organization in the Brain
Frank Rosenblatt · 1958
Earlier work this paper cites.
The gap function of a convex program
Donald W Hearn · 1982
Earlier work this paper cites.
Least absolute deviations: theory, applications, and algorithms
Peter Bloomfield and William L Steiger · 1983
Earlier work this paper cites.
Support-Vector Networks
Corinna Cortes and Vladimir Vapnik · 1995
Earlier work this paper cites.
Lectures on Polytopes , volume 152 of
Günter M Ziegler · 1995
Earlier work this paper cites.
Regression Shrinkage and Selection via the Lasso
Robert Tibshirani · 1996
Earlier work this paper cites.
A Tutorial on Support Vector Machines for Pattern Recognition
Christopher J C Burges · 1998
Earlier work this paper cites.
Atomic Decomposition by Basis Pursuit
Scott Shaobing Chen, David L Donoho, and Michael A Saunders · 1998
Earlier work this paper cites.
An Equivalence Between Sparse Approximation and Support Vector Machines
Federico Girosi · 1998
Earlier work this paper cites.
From Regression to Classification in Support Vector Machines
Massimiliano Pontil, Ryan Rifkin, and Theodoros Evgeniou · 1998
Earlier work this paper cites.
Ridge Regression Learning Algorithm in Dual Variables
Craig Saunders, Alexander Gammerman, and Volodya Vovk · 1998
Earlier work this paper cites.
Regularization Networks and Support Vector Machines
Theodoros Evgeniou, Massimiliano Pontil, and Tomaso Poggio · 2000
Earlier work this paper cites.
A fast iterative nearest point algorithm for support vector machine classifier design
S Sathiya Keerthi, Shirish K Shevade, Chiranjib Bhattacharyya, and K R K Murthy · 2000
Earlier work this paper cites.
A new approach to variable selection in least squares problems
Michael R Osborne, Brett Presnell, and Berwin A Turlach · 2000
Earlier work this paper cites.
RSVM: Reduced Support Vector Machines
Yuh-Jye Lee and Olvi L Mangasarian · 2001
Earlier work this paper cites.
Learning with kernels
Bernhard Schölkopf and Alex J Smola · 2002
Earlier work this paper cites.
Sparseness of Support Vector Machines—Some Asymptotically Sharp Bounds
Ingo Steinwart · 2003
Cited alongside, same era.
Convex optimization
Stephen P Boyd and Lieven Vandenberghe · 2004
Cited alongside, same era.
Least angle regression
Bradley Efron, Trevor Hastie, Iain Johnstone, and Robert Tibshirani · 2004
Cited alongside, same era.
Gene Selection for Microarray Data
Sepp Hochreiter and Klaus Obermayer · 2004
Cited alongside, same era.
The Entire Regularization Path for the Support Vector Machine
Trevor Hastie, Saharon Rosset, Robert Tibshirani, and Ji Zhu · 2004
Cited alongside, same era.
The Generalized LASSO
Volker Roth · 2004
Cited alongside, same era.
A tutorial on support vector regression
Statistics for High-Dimensional Data - Methods, Theory and Applications
Peter Bühlmann and Sara van de Geer · 2011
Later among the works it cites.
Efficient Learning with Partially Observed Attributes
Nicolò Cesa-Bianchi, Shai Shalev-Shwartz, and Ohad Shamir · 2011
Later among the works it cites.
Beating SGD: Learning SVMs in Sublinear Time
Elad Hazan, Tomer Koren, and Nathan Srebro · 2011
Later among the works it cites.
Strong rules for discarding predictors in lasso-type problems
Robert Tibshirani, Jacob Bien, Jerome Friedman, Trevor Hastie, Noah Simon, Jonathan Taylor, and Ryan J. Tibshirani · 2011
Later among the works it cites.
Approximating parameterized convex optimization problems
Joachim Giesen, Martin Jaggi, and Sören Laue · 2012
Later among the works it cites.
An Exponential Lower Bound On The Complexity Of Regularization Paths
Bernd Gärtner, Martin Jaggi, and Clément Maria · 2012
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Alex J Smola and Bernhard Schölkopf · 2004
Cited alongside, same era.
Classification and Selection of Biomarkers in Genomic Data Using LASSO
Debashis Ghosh and Arul M Chinnaiyan · 2005
Cited alongside, same era.
From Lasso regression to Feature vector machine
Fan Li, Yiming Yang, and Eric P Xing · 2005
Cited alongside, same era.
Core Vector Machines: Fast SVM Training on Very Large Data Sets
Ivor W Tsang, James T Kwok, and Pak-Ming Cheung · 2005
Cited alongside, same era.
Support Vector Machines for Dyadic Data
Sepp Hochreiter and Klaus Obermayer · 2006
Cited alongside, same era.
Coordinate Descent Method for Large-scale L2-loss Linear Support Vector Machines
Kai-Wei Chang, Cho-Jui Hsieh, and Chih-Jen Lin · 2008
Cited alongside, same era.
Later among the works it cites.
Approximating Concavely Parameterized Optimization Problems
Joachim Giesen, Jens Müller, Soeren Laue, and Sascha Swiercy · 2012
Later among the works it cites.
Linear Regression with Limited Observation
Elad Hazan and Tomer Koren · 2012
Later among the works it cites.
Complexity Analysis of the Lasso Regularization Path
Julien Mairal and Bin Yu · 2012
Later among the works it cites.
Sublinear time, measurement-optimal, sparse recovery for all
Ely Porat and Martin J Strauss · 2012
Later among the works it cites.
Local Sparse Coding for Image Classification and Retrieval
Jayaraman J Thiagarajan, Karthikeyan Natesan Ramamurthy, and Andreas Spanias · 2012
Later among the works it cites.
An Equivalence between the Lasso and Support Vector Machines
Martin Jaggi · 2013
Closest in time.
Revisiting Frank-Wolfe: Projection-Free Sparse Convex Optimization
Martin Jaggi · 2013
Closest in time.
Safe Screening of Non-Support Vectors in Pathwise SVM Computation
Kohei Ogawa, Yoshiki Suzuki, and Ichiro Takeuchi · 2013
Closest in time.
Lasso Screening Rules via Dual Polytope Projection
Jie Wang, Binbin Lin, Pinghua Gong, Peter Wonka, and Jieping Ye · 2013
Closest in time.