Online learning with kernels
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Alexander J. Smola | Robert C. Williamson | Jyrki Kivinen | R. C. Williamson | Jyrki Kivinen | Alex Smola
[1] Albert B Novikoff,et al. ON CONVERGENCE PROOFS FOR PERCEPTRONS , 1963 .
[2] G. Wahba,et al. Some results on Tchebycheffian spline functions , 1971 .
[3] P. J. Huber. The 1972 Wald Lecture Robust Statistics: A Review , 1972 .
[4] S. Haykin,et al. Adaptive Filter Theory , 1986 .
[5] N. Littlestone. Learning Quickly When Irrelevant Attributes Abound: A New Linear-Threshold Algorithm , 1987, 28th Annual Symposium on Foundations of Computer Science (sfcs 1987).
[6] O. Mangasarian,et al. Robust linear programming discrimination of two linearly inseparable sets , 1992 .
[7] Philip M. Long,et al. WORST-CASE QUADRATIC LOSS BOUNDS FOR ON-LINE PREDICTION OF LINEAR FUNCTIONS BY GRADIENT DESCENT , 1993 .
[8] Alexander J. Smola,et al. Support Vector Method for Function Approximation, Regression Estimation and Signal Processing , 1996, NIPS.
[9] Philip M. Long,et al. Worst-case quadratic loss bounds for prediction using linear functions and gradient descent , 1996, IEEE Trans. Neural Networks.
[10] Yoav Freund,et al. Large Margin Classification Using the Perceptron Algorithm , 1998, COLT' 98.
[11] Alexander J. Smola,et al. Learning with kernels , 1998 .
[12] Claudio Gentile,et al. The Robustness of the p-Norm Algorithms , 1999, COLT '99.
[13] John C. Platt,et al. Fast training of support vector machines using sequential minimal optimization, advances in kernel methods , 1999 .
[14] Manfred Opper,et al. Sparse Representation for Gaussian Process Models , 2000, NIPS.
[15] Thore Graepel,et al. From Margin to Sparsity , 2000, NIPS.
[16] J. Friedman. Special Invited Paper-Additive logistic regression: A statistical view of boosting , 2000 .
[17] Bernhard Schölkopf,et al. New Support Vector Algorithms , 2000, Neural Computation.
[18] James A. Bucklew,et al. Support vector machine techniques for nonlinear equalization , 2000, IEEE Trans. Signal Process..
[19] Gert Cauwenberghs,et al. Incremental and Decremental Support Vector Machine Learning , 2000, NIPS.
[20] Peter L. Bartlett,et al. Functional Gradient Techniques for Combining Hypotheses , 2000 .
[21] Claudio Gentile,et al. A New Approximate Maximal Margin Classification Algorithm , 2002, J. Mach. Learn. Res..
[22] Bernhard Schölkopf,et al. A Generalized Representer Theorem , 2001, COLT/EuroCOLT.
[23] Manfred K. Warmuth,et al. Tracking a Small Set of Experts by Mixing Past Posteriors , 2003, J. Mach. Learn. Res..
[24] Mark Herbster,et al. Learning Additive Models Online with Fast Evaluating Kernels , 2001, COLT/EuroCOLT.
[25] Mark Herbster,et al. Tracking the Best Linear Predictor , 2001, J. Mach. Learn. Res..
[26] Bernhard Schölkopf,et al. Estimating the Support of a High-Dimensional Distribution , 2001, Neural Computation.
[27] Claudio Gentile,et al. Adaptive and Self-Confident On-Line Learning Algorithms , 2000, J. Comput. Syst. Sci..
[28] Alexander J. Smola,et al. Large Margin Classification for Moving Targets , 2002, ALT.
[29] Ralf Herbrich,et al. Learning Kernel Classifiers: Theory and Algorithms , 2001 .
[30] Alexander J. Smola,et al. Fast Kernels for String and Tree Matching , 2002, NIPS.
[31] Chris Mesterharm,et al. Tracking Linear-threshold Concepts with Winnow , 2003, J. Mach. Learn. Res..
[32] Thomas G. Dietterich,et al. Editors. Advances in Neural Information Processing Systems , 2002 .
[33] Michael Vogt,et al. SMO Algorithms for Support Vector Machines without Bias Term , 2002 .
[34] Peter Auer,et al. Tracking the Best Disjunction , 1998, Machine Learning.
[35] Yi Li,et al. The Relaxed Online Maximum Margin Algorithm , 1999, Machine Learning.
[36] Claudio Gentile,et al. On the generalization ability of on-line learning algorithms , 2001, IEEE Transactions on Information Theory.