Breaking the curse of kernelization: budgeted stochastic gradient descent for large-scale SVM training
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[1] Alexander J. Smola,et al. Bundle Methods for Regularized Risk Minimization , 2010, J. Mach. Learn. Res..
[2] Tu Bao Ho,et al. An efficient method for simplifying support vector machines , 2005, ICML.
[3] Corinna Cortes,et al. Support-Vector Networks , 1995, Machine Learning.
[4] Slobodan Vucetic,et al. Tighter Perceptron with improved dual use of cached data for model representation and validation , 2009, 2009 International Joint Conference on Neural Networks.
[5] Chih-Jen Lin,et al. Training and Testing Low-degree Polynomial Data Mappings via Linear SVM , 2010, J. Mach. Learn. Res..
[6] John C. Platt,et al. Fast training of support vector machines using sequential minimal optimization, advances in kernel methods , 1999 .
[7] F ROSENBLATT,et al. The perceptron: a probabilistic model for information storage and organization in the brain. , 1958, Psychological review.
[8] Tong Zhang,et al. Solving large scale linear prediction problems using stochastic gradient descent algorithms , 2004, ICML.
[9] Jason Weston,et al. Online (and Offline) on an Even Tighter Budget , 2005, AISTATS.
[10] Bernhard Schölkopf,et al. Building Sparse Large Margin Classifiers , 2005, ICML.
[11] Yi Li,et al. The Relaxed Online Maximum Margin Algorithm , 1999, Machine Learning.
[12] Chi-Jen Lu,et al. Tree Decomposition for Large-Scale SVM Problems , 2010, 2010 International Conference on Technologies and Applications of Artificial Intelligence.
[13] Thorsten Joachims,et al. Training linear SVMs in linear time , 2006, KDD '06.
[14] Jianping Fan,et al. Support cluster machine , 2007, ICML '07.
[15] Manfred Opper,et al. Sparse Representation for Gaussian Process Models , 2000, NIPS.
[16] Chih-Jen Lin,et al. A dual coordinate descent method for large-scale linear SVM , 2008, ICML '08.
[17] Gert Cauwenberghs,et al. Incremental and Decremental Support Vector Machine Learning , 2000, NIPS.
[18] Richard O. Duda,et al. Pattern classification and scene analysis , 1974, A Wiley-Interscience publication.
[19] Alexander J. Smola,et al. Online learning with kernels , 2001, IEEE Transactions on Signal Processing.
[20] Chih-Jen Lin,et al. A comparison of methods for multiclass support vector machines , 2002, IEEE Trans. Neural Networks.
[21] Sören Sonnenburg,et al. COFFIN: A Computational Framework for Linear SVMs , 2010, ICML.
[22] Kim Stevenson,et al. Online and Offline , 2013 .
[23] Claudio Gentile,et al. A New Approximate Maximal Margin Classification Algorithm , 2002, J. Mach. Learn. Res..
[24] Barbara Caputo,et al. Bounded Kernel-Based Online Learning , 2009, J. Mach. Learn. Res..
[25] Ilya Sutskever,et al. A simpler unified analysis of budget perceptrons , 2009, ICML '09.
[26] Yoram Singer,et al. Pegasos: primal estimated sub-gradient solver for SVM , 2011, Math. Program..
[27] Yoram Singer,et al. The Forgetron: A Kernel-Based Perceptron on a Budget , 2008, SIAM J. Comput..
[28] Chih-Jen Lin,et al. Large Linear Classification When Data Cannot Fit in Memory , 2011, TKDD.
[29] Jason Weston,et al. Trading convexity for scalability , 2006, ICML.
[30] Y. Singer,et al. Logarithmic Regret Algorithms for Strongly Convex Repeated Games , 2007 .
[31] Jason Weston,et al. Fast Kernel Classifiers with Online and Active Learning , 2005, J. Mach. Learn. Res..
[32] Chih-Jen Lin,et al. LIBSVM: A library for support vector machines , 2011, TIST.
[33] Koby Crammer,et al. Online Passive-Aggressive Algorithms , 2003, J. Mach. Learn. Res..
[34] Koby Crammer,et al. Trading representability for scalability: adaptive multi-hyperplane machine for nonlinear classification , 2011, KDD.
[35] Claudio Gentile,et al. Tracking the best hyperplane with a simple budget Perceptron , 2006, Machine Learning.
[36] Christopher J. C. Burges,et al. Simplified Support Vector Decision Rules , 1996, ICML.
[37] Slobodan Vucetic,et al. Online training on a budget of support vector machines using twin prototypes , 2010, Stat. Anal. Data Min..
[38] Patrick Gallinari,et al. SGD-QN: Careful Quasi-Newton Stochastic Gradient Descent , 2009, J. Mach. Learn. Res..
[39] Shie Mannor,et al. Sparse Online Greedy Support Vector Regression , 2002, ECML.
[40] Benjamin Recht,et al. Random Features for Large-Scale Kernel Machines , 2007, NIPS.
[41] Slobodan Vucetic,et al. Online Passive-Aggressive Algorithms on a Budget , 2010, AISTATS.
[42] Koby Crammer,et al. On the Algorithmic Implementation of Multiclass Kernel-based Vector Machines , 2002, J. Mach. Learn. Res..
[43] Ivor W. Tsang,et al. Core Vector Machines: Fast SVM Training on Very Large Data Sets , 2005, J. Mach. Learn. Res..
[44] Yuh-Jye Lee,et al. RSVM: Reduced Support Vector Machines , 2001, SDM.
[45] Slobodan Vucetic,et al. Online training on a budget of support vector machines using twin prototypes , 2010 .
[46] Koby Crammer,et al. Ultraconservative Online Algorithms for Multiclass Problems , 2001, J. Mach. Learn. Res..
[47] Zheng Chen,et al. P-packSVM: Parallel Primal grAdient desCent Kernel SVM , 2009, 2009 Ninth IEEE International Conference on Data Mining.
[48] Dale Schuurmans,et al. implicit Online Learning with Kernels , 2006, NIPS.
[49] Martin Zinkevich,et al. Online Convex Programming and Generalized Infinitesimal Gradient Ascent , 2003, ICML.
[50] Koby Crammer,et al. Online Classification on a Budget , 2003, NIPS.
[51] Gunnar Rätsch,et al. Input space versus feature space in kernel-based methods , 1999, IEEE Trans. Neural Networks.
[52] Yoram Singer,et al. Support Vector Machines on a Budget , 2006, NIPS.
[53] S. Sathiya Keerthi,et al. Building Support Vector Machines with Reduced Classifier Complexity , 2006, J. Mach. Learn. Res..
[54] Ingo Steinwart,et al. Sparseness of Support Vector Machines , 2003, J. Mach. Learn. Res..
[55] Igor Durdanovic,et al. Parallel Support Vector Machines: The Cascade SVM , 2004, NIPS.