On Coresets for Support Vector Machines

[1]  D. Rus,et al.  Provable Filter Pruning for Efficient Neural Networks , 2019, ICLR.

[2]  Dan Feldman,et al.  Core‐sets: An updated survey , 2019, WIREs Data Mining Knowl. Discov..

[3]  Dan Feldman,et al.  Deterministic Coresets for Stochastic Matrices with Applications to Scalable Sparse PageRank , 2019, TAMC.

[4]  Konstantin Makarychev,et al.  Performance of Johnson-Lindenstrauss transform for k-means and k-medians clustering , 2018, STOC.

[5]  Dan Feldman,et al.  Data-Dependent Coresets for Compressing Neural Networks with Applications to Generalization Bounds , 2018, ICLR.

[6]  Andreas Krause,et al.  Training Mixture Models at Scale via Coresets , 2017 .

[7]  Andreas Krause,et al.  Practical Coreset Constructions for Machine Learning , 2017, 1703.06476.

[8]  Christopher Ré,et al.  Weighted SGD for ℓp Regression with Randomized Preconditioning , 2015, SODA.

[9]  Vladimir Braverman,et al.  New Frameworks for Offline and Streaming Coreset Constructions , 2016, ArXiv.

[10]  Michael B. Cohen,et al.  Dimensionality Reduction for k-Means Clustering and Low Rank Approximation , 2014, STOC.

[11]  Sharath Raghvendra,et al.  Accurate Streaming Support Vector Machines , 2014, ArXiv.

[12]  Pramod P. Khargonekar,et al.  Fast SVM training using approximate extreme points , 2013, J. Mach. Learn. Res..

[13]  Trupti M. Kodinariya,et al.  Review on determining number of Cluster in K-Means Clustering , 2013 .

[14]  Nathan Srebro,et al.  Beating SGD: Learning SVMs in Sublinear Time , 2011, NIPS.

[15]  Michael Langberg,et al.  A unified framework for approximating and clustering data , 2011, STOC.

[16]  David P. Woodruff,et al.  Sublinear Optimization for Machine Learning , 2010, 2010 IEEE 51st Annual Symposium on Foundations of Computer Science.

[17]  Pankaj K. Agarwal,et al.  Streaming Algorithms for Extent Problems in High Dimensions , 2010, SODA '10.

[18]  L. Schulman,et al.  Universal ε-approximators for integrals , 2010, SODA '10.

[19]  Suresh Venkatasubramanian,et al.  Streamed Learning: One-Pass SVMs , 2009, IJCAI.

[20]  Martin Jaggi,et al.  Coresets for polytope distance , 2009, SCG '09.

[21]  Kenneth L. Clarkson,et al.  Coresets, sparse greedy approximation, and the Frank-Wolfe algorithm , 2008, SODA '08.

[22]  Stéphane Canu,et al.  Comments on the "Core Vector Machines: Fast SVM Training on Very Large Data Sets" , 2007, J. Mach. Learn. Res..

[23]  Yoram Singer,et al.  Pegasos: primal estimated sub-gradient solver for SVM , 2007, ICML '07.

[24]  I. Tsang,et al.  Simpler core vector machines with enclosing balls , 2007, ICML '07.

[25]  Sergei Vassilvitskii,et al.  k-means++: the advantages of careful seeding , 2007, SODA '07.

[26]  Dan Roth,et al.  Maximum Margin Coresets for Active and Noise Tolerant Learning , 2007, IJCAI.

[27]  Kasturi R. Varadarajan,et al.  Geometric Approximation via Coresets , 2007 .

[28]  Thorsten Joachims,et al.  Training linear SVMs in linear time , 2006, KDD '06.

[29]  Ivor W. Tsang,et al.  Core Vector Machines: Fast SVM Training on Very Large Data Sets , 2005, J. Mach. Learn. Res..

[30]  Sariel Har-Peled,et al.  On coresets for k-means and k-median clustering , 2004, STOC '04.

[31]  Kenneth L. Clarkson,et al.  Smaller core-sets for balls , 2003, SODA '03.

[32]  Yi Li,et al.  Improved bounds on the sample complexity of learning , 2000, SODA '00.

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

[34]  C. Lingard,et al.  Book Review: The Challenge of Red China , 1946 .