暂无分享,去创建一个
[1] G. Wahba,et al. Some results on Tchebycheffian spline functions , 1971 .
[2] Nir Ailon,et al. Fast Dimension Reduction Using Rademacher Series on Dual BCH Codes , 2008, SODA '08.
[3] Joel A. Tropp,et al. Improved Analysis of the subsampled Randomized Hadamard Transform , 2010, Adv. Data Sci. Adapt. Anal..
[4] Petros Drineas,et al. On the Nyström Method for Approximating a Gram Matrix for Improved Kernel-Based Learning , 2005, J. Mach. Learn. Res..
[5] Tamás Sarlós,et al. Improved Approximation Algorithms for Large Matrices via Random Projections , 2006, 2006 47th Annual IEEE Symposium on Foundations of Computer Science (FOCS'06).
[6] Nathan Halko,et al. Finding Structure with Randomness: Probabilistic Algorithms for Constructing Approximate Matrix Decompositions , 2009, SIAM Rev..
[7] Michael W. Mahoney. Randomized Algorithms for Matrices and Data , 2011, Found. Trends Mach. Learn..
[8] Shahar Mendelson,et al. Geometric Parameters of Kernel Machines , 2002, COLT.
[9] Joel A. Tropp,et al. Living on the edge: phase transitions in convex programs with random data , 2013, 1303.6672.
[10] Matthias W. Seeger,et al. Using the Nyström Method to Speed Up Kernel Machines , 2000, NIPS.
[11] Martin J. Wainwright,et al. Iterative Hessian Sketch: Fast and Accurate Solution Approximation for Constrained Least-Squares , 2014, J. Mach. Learn. Res..
[12] Martin J. Wainwright,et al. Minimax-Optimal Rates For Sparse Additive Models Over Kernel Classes Via Convex Programming , 2010, J. Mach. Learn. Res..
[13] Martin J. Wainwright,et al. Randomized sketches of convex programs with sharp guarantees , 2014, 2014 IEEE International Symposium on Information Theory.
[14] S. R. Jammalamadaka,et al. Empirical Processes in M-Estimation , 2001 .
[15] Jiri Matousek,et al. Lectures on discrete geometry , 2002, Graduate texts in mathematics.
[16] C. J. Stone,et al. Optimal Global Rates of Convergence for Nonparametric Regression , 1982 .
[17] Martin J. Wainwright,et al. Divide and Conquer Kernel Ridge Regression , 2013, COLT.
[18] P. Bartlett,et al. Local Rademacher complexities , 2005, math/0508275.
[19] A. Berlinet,et al. Reproducing kernel Hilbert spaces in probability and statistics , 2004 .
[20] Michael W. Mahoney,et al. Revisiting the Nystrom Method for Improved Large-scale Machine Learning , 2013, J. Mach. Learn. Res..
[21] N. Aronszajn. Theory of Reproducing Kernels. , 1950 .
[22] V. Koltchinskii. Local Rademacher complexities and oracle inequalities in risk minimization , 2006, 0708.0083.
[23] Santosh S. Vempala,et al. The Random Projection Method , 2005, DIMACS Series in Discrete Mathematics and Theoretical Computer Science.
[24] M. Ledoux. The concentration of measure phenomenon , 2001 .
[25] Hans C. van Houwelingen,et al. The Elements of Statistical Learning, Data Mining, Inference, and Prediction. Trevor Hastie, Robert Tibshirani and Jerome Friedman, Springer, New York, 2001. No. of pages: xvi+533. ISBN 0‐387‐95284‐5 , 2004 .
[26] Alexander Gammerman,et al. Ridge Regression Learning Algorithm in Dual Variables , 1998, ICML.
[27] G. Wahba. Spline models for observational data , 1990 .
[28] Francis R. Bach,et al. Sharp analysis of low-rank kernel matrix approximations , 2012, COLT.
[29] David P. Woodruff,et al. Optimal Approximate Matrix Product in Terms of Stable Rank , 2015, ICALP.
[30] Nello Cristianini,et al. Kernel Methods for Pattern Analysis , 2003, ICTAI.
[31] Christos Boutsidis,et al. Improved Matrix Algorithms via the Subsampled Randomized Hadamard Transform , 2012, SIAM J. Matrix Anal. Appl..
[32] Ashutosh Kumar Singh,et al. The Elements of Statistical Learning: Data Mining, Inference, and Prediction , 2010 .
[33] Joel A. Tropp,et al. User-Friendly Tail Bounds for Sums of Random Matrices , 2010, Found. Comput. Math..
[34] Michael W. Mahoney,et al. Fast Randomized Kernel Methods With Statistical Guarantees , 2014, ArXiv.
[35] Chong Gu. Smoothing Spline Anova Models , 2002 .
[36] Rachel Ward,et al. New and Improved Johnson-Lindenstrauss Embeddings via the Restricted Isometry Property , 2010, SIAM J. Math. Anal..