Trading representability for scalability: adaptive multi-hyperplane machine for nonlinear classification
暂无分享,去创建一个
Koby Crammer | Slobodan Vucetic | Nemanja Djuric | Zhuang Wang | K. Crammer | Nemanja Djuric | S. Vucetic | Zhuang Wang
[1] Thorsten Joachims,et al. Training linear SVMs in linear time , 2006, KDD '06.
[2] John C. Platt,et al. Fast training of support vector machines using sequential minimal optimization, advances in kernel methods , 1999 .
[3] Chih-Jen Lin,et al. Training and Testing Low-degree Polynomial Data Mappings via Linear SVM , 2010, J. Mach. Learn. Res..
[4] Jason Weston,et al. Trading convexity for scalability , 2006, ICML.
[5] Y. Singer,et al. Logarithmic Regret Algorithms for Strongly Convex Repeated Games , 2007 .
[6] Ivor W. Tsang,et al. Core Vector Machines: Fast SVM Training on Very Large Data Sets , 2005, J. Mach. Learn. Res..
[7] S. Canu,et al. Training Invariant Support Vector Machines using Selective Sampling , 2005 .
[8] Slobodan Vucetic,et al. Online training on a budget of support vector machines using twin prototypes , 2010 .
[9] Chih-Jen Lin,et al. LIBSVM: A library for support vector machines , 2011, TIST.
[10] Ingo Steinwart,et al. Sparseness of Support Vector Machines , 2003, J. Mach. Learn. Res..
[11] Sören Sonnenburg,et al. COFFIN: A Computational Framework for Linear SVMs , 2010, ICML.
[12] Patrick Gallinari,et al. SGD-QN: Careful Quasi-Newton Stochastic Gradient Descent , 2009, J. Mach. Learn. Res..
[13] Zheng Chen,et al. P-packSVM: Parallel Primal grAdient desCent Kernel SVM , 2009, 2009 Ninth IEEE International Conference on Data Mining.
[14] Benjamin Recht,et al. Random Features for Large-Scale Kernel Machines , 2007, NIPS.
[15] Lawrence K. Saul,et al. Identifying suspicious URLs: an application of large-scale online learning , 2009, ICML '09.
[16] Chih-Jen Lin,et al. Large Linear Classification When Data Cannot Fit in Memory , 2011, TKDD.
[17] Alessandro Sperduti,et al. Multiclass Classification with Multi-Prototype Support Vector Machines , 2005, J. Mach. Learn. Res..
[18] Koby Crammer,et al. On the Algorithmic Implementation of Multiclass Kernel-based Vector Machines , 2002, J. Mach. Learn. Res..
[19] Koby Crammer,et al. Multi-Class Pegasos on a Budget , 2010, ICML.
[20] Yoram Singer,et al. Pegasos: primal estimated sub-gradient solver for SVM , 2011, Math. Program..
[21] Slobodan Vucetic,et al. Online training on a budget of support vector machines using twin prototypes , 2010, Stat. Anal. Data Min..
[22] Chih-Jen Lin,et al. A dual coordinate descent method for large-scale linear SVM , 2008, ICML '08.
[23] Jason Weston,et al. Fast Kernel Classifiers with Online and Active Learning , 2005, J. Mach. Learn. Res..
[24] Alexander J. Smola,et al. Bundle Methods for Regularized Risk Minimization , 2010, J. Mach. Learn. Res..
[25] Corinna Cortes,et al. Support-Vector Networks , 1995, Machine Learning.
[26] Nello Cristianini,et al. Large Margin DAGs for Multiclass Classification , 1999, NIPS.
[27] Tong Zhang,et al. Solving large scale linear prediction problems using stochastic gradient descent algorithms , 2004, ICML.
[28] Alexander J. Smola,et al. Online learning with kernels , 2001, IEEE Transactions on Signal Processing.