Denoising and Dimension Reduction in Feature Space
暂无分享,去创建一个
[1] Mikio L. Braun,et al. Accurate Error Bounds for the Eigenvalues of the Kernel Matrix , 2006, J. Mach. Learn. Res..
[2] Tong Zhang,et al. Learning Bounds for Kernel Regression Using Effective Data Dimensionality , 2005, Neural Computation.
[3] Mathematisch-Naturwissenschaftlichen Fakult,et al. Spectral Properties of the Kernel Matrix and their Relation to Kernel Methods in Machine Learning , 2005 .
[4] V. Koltchinskii,et al. Random matrix approximation of spectra of integral operators , 2000 .
[5] Gunnar Rätsch,et al. Kernel PCA and De-Noising in Feature Spaces , 1998, NIPS.
[6] Gunnar Rätsch,et al. Input space versus feature space in kernel-based methods , 1999, IEEE Trans. Neural Networks.
[7] G. Wahba. Spline models for observational data , 1990 .
[8] Gunnar Rätsch,et al. Soft Margins for AdaBoost , 2001, Machine Learning.
[9] V. Koltchinskii. Asymptotics of Spectral Projections of Some Random Matrices Approximating Integral Operators , 1998 .
[10] Bernhard Schölkopf,et al. Nonlinear Component Analysis as a Kernel Eigenvalue Problem , 1998, Neural Computation.
[11] Vladimir Vapnik,et al. Statistical learning theory , 1998 .
[12] Alexander J. Smola,et al. Learning with kernels , 1998 .
[13] Gilles Blanchard,et al. On the Convergence of Eigenspaces in Kernel Principal Component Analysis , 2005, NIPS.
[14] Gunnar Rätsch,et al. An introduction to kernel-based learning algorithms , 2001, IEEE Trans. Neural Networks.