Learning with kernels
暂无分享,去创建一个
[1] J. Mercer. Functions of Positive and Negative Type, and their Connection with the Theory of Integral Equations , 1909 .
[2] J. Mercer. Functions of positive and negative type, and their connection with the theory of integral equations , 1909 .
[3] N. Aronszajn. Theory of Reproducing Kernels. , 1950 .
[4] R. Fortet,et al. Convergence de la répartition empirique vers la répartition théorique , 1953 .
[5] J. Davenport. Editor , 1960 .
[6] V. Vapnik. Pattern recognition using generalized portrait method , 1963 .
[7] V. Vapnik,et al. A note one class of perceptrons , 1964 .
[8] M. Aizerman,et al. Theoretical Foundations of the Potential Function Method in Pattern Recognition Learning , 1964 .
[9] Shun-ichi Amari,et al. A Theory of Pattern Recognition , 1968 .
[10] G. Wahba,et al. A Correspondence Between Bayesian Estimation on Stochastic Processes and Smoothing by Splines , 1970 .
[11] G. Wahba,et al. Some results on Tchebycheffian spline functions , 1971 .
[12] Keinosuke Fukunaga,et al. Introduction to Statistical Pattern Recognition , 1972 .
[13] Richard O. Duda,et al. Pattern classification and scene analysis , 1974, A Wiley-Interscience publication.
[14] J. W. Humberston. Classical mechanics , 1980, Nature.
[15] H. Weinert. Reproducing kernel Hilbert spaces: Applications in statistical signal processing , 1982 .
[16] C. Berg,et al. Harmonic Analysis on Semigroups , 1984 .
[17] Saburou Saitoh,et al. Theory of Reproducing Kernels and Its Applications , 1988 .
[18] Martin Casdagli,et al. Nonlinear prediction of chaotic time series , 1989 .
[19] F. Girosi,et al. Networks for approximation and learning , 1990, Proc. IEEE.
[20] G. Wahba. Spline models for observational data , 1990 .
[21] D. Cox,et al. Asymptotic Analysis of Penalized Likelihood and Related Estimators , 1990 .
[22] Bernhard E. Boser,et al. A training algorithm for optimal margin classifiers , 1992, COLT '92.
[23] Isabelle Guyon,et al. Automatic Capacity Tuning of Very Large VC-Dimension Classifiers , 1992, NIPS.
[24] Robert E. Schapire,et al. Efficient distribution-free learning of probabilistic concepts , 1990, Proceedings [1990] 31st Annual Symposium on Foundations of Computer Science.
[25] Bernhard Schölkopf,et al. Improving the Accuracy and Speed of Support Vector Machines , 1996, NIPS.
[26] László Györfi,et al. A Probabilistic Theory of Pattern Recognition , 1996, Stochastic Modelling and Applied Probability.
[27] Alexander J. Smola,et al. Support Vector Method for Function Approximation, Regression Estimation and Signal Processing , 1996, NIPS.
[28] Bernhard Schölkopf,et al. Incorporating Invariances in Support Vector Learning Machines , 1996, ICANN.
[29] Bernhard Schölkopf,et al. Support vector learning , 1997 .
[30] Federico Girosi,et al. An improved training algorithm for support vector machines , 1997, Neural Networks for Signal Processing VII. Proceedings of the 1997 IEEE Signal Processing Society Workshop.
[31] Noga Alon,et al. Scale-sensitive dimensions, uniform convergence, and learnability , 1997, JACM.
[32] Gunnar Rätsch,et al. Predicting Time Series with Support Vector Machines , 1997, ICANN.
[33] Bernhard Schölkopf,et al. On a Kernel-Based Method for Pattern Recognition, Regression, Approximation, and Operator Inversion , 1998, Algorithmica.
[34] Christopher K. I. Williams. Prediction with Gaussian Processes: From Linear Regression to Linear Prediction and Beyond , 1999, Learning in Graphical Models.
[35] Vladimir Vapnik,et al. Statistical learning theory , 1998 .
[36] John Shawe-Taylor,et al. Generalization Performance of Support Vector Machines and Other Pattern Classifiers , 1999 .
[37] Robert J. Vanderbei,et al. Linear Programming: Foundations and Extensions , 1998, Kluwer international series in operations research and management service.
[38] J. C. BurgesChristopher. A Tutorial on Support Vector Machines for Pattern Recognition , 1998 .
[39] Bernhard Schölkopf,et al. The connection between regularization operators and support vector kernels , 1998, Neural Networks.
[40] Federico Girosi,et al. An Equivalence Between Sparse Approximation and Support Vector Machines , 1998, Neural Computation.
[41] Alexander J. Smola,et al. Support Vector Machine Reference Manual , 1998 .
[42] J. Weston,et al. Support vector regression with ANOVA decomposition kernels , 1999 .
[43] Simon Haykin,et al. Support vector machines for dynamic reconstruction of a chaotic system , 1999 .
[44] Christopher J. C. Burges,et al. Geometry and invariance in kernel based methods , 1999 .
[45] John C. Platt,et al. Fast training of support vector machines using sequential minimal optimization, advances in kernel methods , 1999 .
[46] David Haussler,et al. Convolution kernels on discrete structures , 1999 .
[47] B. Schölkopf,et al. Advances in kernel methods: support vector learning , 1999 .
[48] Gunnar Rätsch,et al. Input space versus feature space in kernel-based methods , 1999, IEEE Trans. Neural Networks.
[49] Vladimir Vapnik,et al. Three remarks on the support vector method of function estimation , 1999 .
[50] Jason Weston. Leave-One-Out Support Vector Machines , 1999, IJCAI.
[51] Theodore Johnson,et al. Squashing flat files flatter , 1999, KDD '99.
[52] Nello Cristianini,et al. An introduction to Support Vector Machines , 2000 .
[53] Vladimir N. Vapnik,et al. The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.
[54] Bernhard Schölkopf,et al. New Support Vector Algorithms , 2000, Neural Computation.
[55] Alexander J. Smola,et al. Advances in Large Margin Classifiers , 2000 .
[56] Tomaso A. Poggio,et al. Regularization Networks and Support Vector Machines , 2000, Adv. Comput. Math..