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We consider supervised learning problems within the positive-definite kernel framework, such as kernel ridge regression, kernel logistic regression or the support vector machine.
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Fast sparse Gaussian process methods: The informative vector machine
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Kernel projection machine: a new tool for pattern recognition
G. Blanchard, P. Massart, R. Vert, and L. Zwald · 2004
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Kernel methods in computational biology
B. Schölkopf, K. Tsuda, and J.P. Vert · 2004
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J. Shawe-Taylor and N. Cristianini · 2004
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Predictive low-rank decomposition for kernel methods
F. Bach and M. I. Jordan · 2005
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A. Bordes, S. Ertekin, J. Weston, and L. Bottou · 2005
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The Forgetron: A kernel-based perceptron on a fixed budget
O. Dekel, S. Shalev-Shwartz, and Y. Singer · 2005
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Toeplitz and circulant matrices: A review
R. M. Gray · 2006
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Building support vector machines with reduced classifier complexity
S. S. Keerthi, O. Chapelle, and D. DeCoste · 2006
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Gaussian Processes for Machine Learning
C. Rasmussen and C. Williams · 2006
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Optimal rates for the regularized least-squares algorithm
A. Caponnetto and E. De Vito · 2007
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Training a support vector machine in the primal
O. Chapelle · 2007
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Optimal rates for regularized least squares regression
I. Steinwart, D. Hush, C. Scovel, et al · 2009
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Self-concordant analysis for logistic regression
F. Bach · 2010
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On the impact of kernel approximation on learning accuracy
C. Cortes, M. Mohri, and A. Talwalkar · 2010
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Tight sample complexity of large-margin learning
S. Sabato, N. Srebro, and N. Tishby · 2010
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Matrix coherence and the nyström method
A. Talwalkar and A. Rostamizadeh · 2010
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A risk comparison of ordinary least squares vs. ridge regression
P. S. Dhillon, D. P. Foster, S. M. Kakade, and L. H. Ungar · 2011
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Value regularization and Fenchel duality
R. M. Rifkin and R. A. Lippert · 2007
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Pegasos: Primal estimated sub-gradient solver for SVM
S. Shalev-Shwartz, Y. Singer, and N. Srebro · 2007
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Local features and kernels for classification of texture and object categories: A comprehensive study
J. Zhang, M. Marszałek, S. Lazebnik, and C. Schmid · 2007
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Statistical performance of support vector machines
G. Blanchard, O. Bousquet, and P. Massart · 2008
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Testing for homogeneity with kernel Fisher discriminant analysis
Z. Harchaoui, F. Bach, and E. Moulines · 2008
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An analysis of random design linear regression
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Randomized algorithms for matrices and data
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Online learning as stochastic approximation of regularization paths
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Improved analysis of the subsampled randomized Hadamard transform
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Fast approximation of matrix coherence and statistical leverage
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Tail inequalities for sums of random matrices that depend on the intrinsic dimension
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Sampling methods for the Nyström method
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User-friendly tail bounds for sums of random matrices
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Breaking the curse of kernelization: Budgeted stochastic gradient descent for large-scale SVM training
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Nyström method vs. random fourier features: A theoretical and empirical comparison
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