The kernel recursive least-squares algorithm
暂无分享,去创建一个
Shie Mannor | Ron Meir | Yaakov Engel | Shie Mannor | Y. Engel | R. Meir
[1] G. Wahba,et al. Some results on Tchebycheffian spline functions , 1971 .
[2] L. Glass,et al. Oscillation and chaos in physiological control systems. , 1977, Science.
[3] Edward J. Wegman,et al. Statistical Signal Processing , 1985 .
[4] Lennart Ljung,et al. System Identification: Theory for the User , 1987 .
[5] G. Wahba. Spline models for observational data , 1990 .
[6] J. Friedman. Multivariate adaptive regression splines , 1990 .
[7] T. Kailath,et al. A state-space approach to adaptive RLS filtering , 1994, IEEE Signal Processing Magazine.
[8] A. Weigend,et al. Time Series Prediction: Forecasting the Future and Understanding the Past , 1994 .
[9] Andreas S. Weigend,et al. Time Series Prediction: Forecasting the Future and Understanding the Past , 1994 .
[10] Balas K. Natarajan,et al. Sparse Approximate Solutions to Linear Systems , 1995, SIAM J. Comput..
[11] Christopher J. C. Burges,et al. Simplified Support Vector Decision Rules , 1996, ICML.
[12] Bernhard Schölkopf,et al. Improving the Accuracy and Speed of Support Vector Machines , 1996, NIPS.
[13] Alexander J. Smola,et al. Support Vector Method for Function Approximation, Regression Estimation and Signal Processing , 1996, NIPS.
[14] Jon A. Wellner,et al. Weak Convergence and Empirical Processes: With Applications to Statistics , 1996 .
[15] Steve Rogers,et al. Adaptive Filter Theory , 1996 .
[16] M. Gibbs,et al. Efficient implementation of gaussian processes , 1997 .
[17] Gunnar Rätsch,et al. Using support vector machines for time series prediction , 1999 .
[18] Christopher K. I. Williams. Prediction with Gaussian Processes: From Linear Regression to Linear Prediction and Beyond , 1999, Learning in Graphical Models.
[19] Federico Girosi,et al. Reducing the run-time complexity of Support Vector Machines , 1999 .
[20] Vladimir Vapnik,et al. Statistical learning theory , 1998 .
[21] Bernhard Schölkopf,et al. Nonlinear Component Analysis as a Kernel Eigenvalue Problem , 1998, Neural Computation.
[22] S. Mallat. A wavelet tour of signal processing , 1998 .
[23] Alexander J. Smola,et al. Learning with kernels , 1998 .
[24] John C. Platt,et al. Fast training of support vector machines using sequential minimal optimization, advances in kernel methods , 1999 .
[25] Peter L. Bartlett,et al. Neural Network Learning - Theoretical Foundations , 1999 .
[26] Gunnar Rätsch,et al. Input space versus feature space in kernel-based methods , 1999, IEEE Trans. Neural Networks.
[27] Manfred Opper,et al. Sparse Representation for Gaussian Process Models , 2000, NIPS.
[28] Vladimir N. Vapnik,et al. The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.
[29] Alexander J. Smola,et al. Sparse Greedy Gaussian Process Regression , 2000, NIPS.
[30] B. Schölkopf,et al. Sparse Greedy Matrix Approximation for Machine Learning , 2000, ICML.
[31] Michael E. Tipping. Sparse Kernel Principal Component Analysis , 2000, NIPS.
[32] Christopher K. I. Williams,et al. Using the Nyström Method to Speed Up Kernel Machines , 2000, NIPS.
[33] Tomaso A. Poggio,et al. Regularization Networks and Support Vector Machines , 2000, Adv. Comput. Math..
[34] James A. Bucklew,et al. Support vector machine techniques for nonlinear equalization , 2000, IEEE Trans. Signal Process..
[35] Katya Scheinberg,et al. Efficient SVM Training Using Low-Rank Kernel Representations , 2002, J. Mach. Learn. Res..
[36] Samy Bengio,et al. SVMTorch: Support Vector Machines for Large-Scale Regression Problems , 2001, J. Mach. Learn. Res..
[37] Stefan Rüping,et al. Incremental Learning with Support Vector Machines , 2001, ICDM.
[38] Tom Downs,et al. Exact Simplification of Support Vector Solutions , 2002, J. Mach. Learn. Res..
[39] George Eastman House,et al. Sparse Bayesian Learning and the Relevance Vector Machine , 2001 .
[40] Venkataramanan Balakrishnan,et al. System identification: theory for the user (second edition): Lennart Ljung; Prentice-Hall, Englewood Cliffs, NJ, 1999, ISBN 0-13-656695-2 , 2002, Autom..
[41] Lehel Csató,et al. Sparse On-Line Gaussian Processes , 2002, Neural Computation.
[42] Dustin Boswell,et al. Introduction to Support Vector Machines , 2002 .
[43] Shie Mannor,et al. Sparse Online Greedy Support Vector Regression , 2002, ECML.
[44] Tong Zhang,et al. Sequential greedy approximation for certain convex optimization problems , 2003, IEEE Trans. Inf. Theory.
[45] Christopher K. I. Williams. Learning Kernel Classifiers , 2003 .
[46] Pascal Vincent,et al. Kernel Matching Pursuit , 2002, Machine Learning.
[47] Peter Sollich,et al. Bayesian Methods for Support Vector Machines: Evidence and Predictive Class Probabilities , 2002, Machine Learning.