Learning with kernels
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[1] Bernhard Schölkopf,et al. A Kernel Approach for Learning from Almost Orthogonal Patterns , 2002, PKDD.
[2] Gunnar Rätsch,et al. A New Discriminative Kernel from Probabilistic Models , 2001, Neural Computation.
[3] Bernhard Schölkopf,et al. Kernel Dependency Estimation , 2002, NIPS.
[4] Jean-Philippe Vert. A tree kernel to analyze phylog enetic profi les , 2002 .
[5] Risi Kondor,et al. Diffusion kernels on graphs and other discrete structures , 2002, ICML 2002.
[6] Daphne Koller,et al. Support Vector Machine Active Learning with Applications to Text Classification , 2000, J. Mach. Learn. Res..
[7] Bernhard Schölkopf,et al. Estimating the Support of a High-Dimensional Distribution , 2001, Neural Computation.
[8] David C. Gibbon,et al. Relevance Feedback using Support Vector Machines , 2001, ICML.
[9] Sayan Mukherjee,et al. Molecular classification of multiple tumor types , 2001, ISMB.
[10] Jason Weston,et al. Gene functional classification from heterogeneous data , 2001, RECOMB.
[11] Gunnar Rätsch,et al. Active Learning in the Drug Discovery Process , 2001, NIPS.
[12] Motoaki Kawanabe,et al. Kernel Feature Spaces and Nonlinear Blind Souce Separation , 2001, NIPS.
[13] Bernhard Schölkopf,et al. Some kernels for structured data , 2001 .
[14] Bernhard Schölkopf,et al. A Kernel Approach for Vector Quantization with Guaranteed Distortion Bounds , 2001, AISTATS.
[15] Nello Cristianini,et al. Classification using String Kernels , 2000 .
[16] Nello Cristianini,et al. Support vector machine classification and validation of cancer tissue samples using microarray expression data , 2000, Bioinform..
[17] Sheng Chen,et al. Design of the optimal separating hyperplane for the decision feedback equalizer using support vector machines , 2000, 2000 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.00CH37100).
[18] Bernhard Schölkopf,et al. New Support Vector Algorithms , 2000, Neural Computation.
[19] Tomaso A. Poggio,et al. Regularization Networks and Support Vector Machines , 2000, Adv. Comput. Math..
[20] Nello Cristianini,et al. An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .
[21] David Haussler,et al. A Discriminative Framework for Detecting Remote Protein Homologies , 2000, J. Comput. Biol..
[22] Vladimir N. Vapnik,et al. The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.
[23] Gunnar Rätsch,et al. Engineering Support Vector Machine Kerneis That Recognize Translation Initialion Sites , 2000, German Conference on Bioinformatics.
[24] Gunnar Rätsch,et al. Input space versus feature space in kernel-based methods , 1999, IEEE Trans. Neural Networks.
[25] John C. Platt,et al. Fast training of support vector machines using sequential minimal optimization, advances in kernel methods , 1999 .
[26] J. Weston,et al. Support vector regression with ANOVA decomposition kernels , 1999 .
[27] M. Seeger. Bayesian methods for Support Vector machines and Gaussian processes , 1999 .
[28] David Haussler,et al. Probabilistic kernel regression models , 1999, AISTATS.
[29] Shaogang Gong,et al. A Multi-View Nonlinear Active Shape Model Using Kernel PCA , 1999, BMVC.
[30] David Haussler,et al. Convolution kernels on discrete structures , 1999 .
[31] Vapnik,et al. SVMs for Histogram Based Image Classification , 1999 .
[32] C. Watkins. Dynamic Alignment Kernels , 1999 .
[33] Christopher K. I. Williams. Prediction with Gaussian Processes: From Linear Regression to Linear Prediction and Beyond , 1999, Learning in Graphical Models.
[34] Federico Girosi,et al. An Equivalence Between Sparse Approximation and Support Vector Machines , 1998, Neural Computation.
[35] Bernhard Schölkopf,et al. Nonlinear Component Analysis as a Kernel Eigenvalue Problem , 1998, Neural Computation.
[36] Bernhard Schölkopf,et al. The connection between regularization operators and support vector kernels , 1998, Neural Networks.
[37] Thorsten Joachims,et al. Text Categorization with Support Vector Machines: Learning with Many Relevant Features , 1998, ECML.
[38] Alexander J. Smola,et al. Learning with kernels , 1998 .
[39] Vladimir Vapnik,et al. Statistical learning theory , 1998 .
[40] Yoshua Bengio,et al. Gradient-based learning applied to document recognition , 1998, Proc. IEEE.
[41] Bernhard Schölkopf,et al. Prior Knowledge in Support Vector Kernels , 1997, NIPS.
[42] Federico Girosi,et al. Support Vector Machines: Training and Applications , 1997 .
[43] Bernhard Schölkopf,et al. Comparing support vector machines with Gaussian kernels to radial basis function classifiers , 1997, IEEE Trans. Signal Process..
[44] Bernhard Schölkopf,et al. Improving the accuracy and speed of support vector learning machines , 1997, NIPS 1997.
[45] Bernhard Schölkopf,et al. Support vector learning , 1997 .
[46] Christopher J. C. Burges,et al. Simplified Support Vector Decision Rules , 1996, ICML.
[47] László Györfi,et al. A Probabilistic Theory of Pattern Recognition , 1996, Stochastic Modelling and Applied Probability.
[48] San Cristóbal Mateo,et al. The Lack of A Priori Distinctions Between Learning Algorithms , 1996 .
[49] Bernhard Schölkopf,et al. Extracting Support Data for a Given Task , 1995, KDD.
[50] Isabelle Guyon,et al. Comparison of classifier methods: a case study in handwritten digit recognition , 1994, Proceedings of the 12th IAPR International Conference on Pattern Recognition, Vol. 3 - Conference C: Signal Processing (Cat. No.94CH3440-5).
[51] Yann LeCun,et al. Efficient Pattern Recognition Using a New Transformation Distance , 1992, NIPS.
[52] Bernhard E. Boser,et al. A training algorithm for optimal margin classifiers , 1992, COLT '92.
[53] O. Mangasarian,et al. Robust linear programming discrimination of two linearly inseparable sets , 1992 .
[54] D. Cox,et al. Asymptotic Analysis of Penalized Likelihood and Related Estimators , 1990 .
[55] F. Girosi,et al. Networks for approximation and learning , 1990, Proc. IEEE.
[56] G. Wahba. Spline Models for Observational Data , 1990 .
[57] Saburou Saitoh,et al. Theory of Reproducing Kernels and Its Applications , 1988 .
[58] C. Berg,et al. Harmonic Analysis on Semigroups , 1984 .
[59] H. Weinert. Reproducing kernel Hilbert spaces: Applications in statistical signal processing , 1982 .
[60] G. Wahba,et al. Some results on Tchebycheffian spline functions , 1971 .
[61] G. Wahba,et al. A Correspondence Between Bayesian Estimation on Stochastic Processes and Smoothing by Splines , 1970 .
[62] Shun-ichi Amari,et al. A Theory of Pattern Recognition , 1968 .
[63] V. Vapnik. Pattern recognition using generalized portrait method , 1963 .
[64] N. Aronszajn. Theory of Reproducing Kernels. , 1950 .
[65] D. Politis,et al. Statistical Estimation , 2022 .
[66] J. Mercer. Functions of Positive and Negative Type, and their Connection with the Theory of Integral Equations , 1909 .
[67] K. Schittkowski,et al. NONLINEAR PROGRAMMING , 2022 .