The following pages link to Learning Theory (Q4680909):
Displaying 39 items.
- Optimal eigen expansions and uniform bounds (Q343789) (← links)
- Universally consistent vertex classification for latent positions graphs (Q366983) (← links)
- VC dimensions of principal component analysis (Q603853) (← links)
- On information plus noise kernel random matrices (Q605943) (← links)
- Oracle inequalities for support vector machines that are based on random entropy numbers (Q731974) (← links)
- Kernel principal component analysis for efficient, differentiable parametrization of multipoint geostatistics (Q930824) (← links)
- Unsupervised slow subspace-learning from stationary processes (Q950200) (← links)
- Error bounds for suboptimal solutions to kernel principal component analysis (Q968012) (← links)
- Adaptive kernel principal component analysis (Q985481) (← links)
- New asymptotic results in principal component analysis (Q1688427) (← links)
- Higher-order principal component analysis for the approximation of tensors in tree-based low-rank formats (Q1728088) (← links)
- Robust dimension-free Gram operator estimates (Q1750105) (← links)
- A class of optimal estimators for the covariance operator in reproducing kernel Hilbert spaces (Q1755120) (← links)
- A note on kernel principal component regression (Q1938788) (← links)
- Efficient estimation of smooth functionals in Gaussian shift models (Q2041800) (← links)
- Principal component analysis for multivariate extremes (Q2044326) (← links)
- Model reduction and neural networks for parametric PDEs (Q2050400) (← links)
- Efficient estimation of linear functionals of principal components (Q2176629) (← links)
- Nonasymptotic upper bounds for the reconstruction error of PCA (Q2196210) (← links)
- Convergence rate of Krasulina estimator (Q2273729) (← links)
- On the predictive potential of kernel principal components (Q2283584) (← links)
- High-probability bounds for the reconstruction error of PCA (Q2307416) (← links)
- Random discretization of the finite Fourier transform and related kernel random matrices (Q2310829) (← links)
- An overview of recent advancements in causal studies (Q2359616) (← links)
- Learning linear PCA with convex semi-definite programming (Q2373456) (← links)
- Statistical properties of kernel principal component analysis (Q2384134) (← links)
- Indefinite kernels in least squares support vector machines and principal component analysis (Q2397170) (← links)
- Statistical performance of support vector machines (Q2426613) (← links)
- Concentration of kernel matrices with application to kernel spectral clustering (Q2656607) (← links)
- Revisiting the predictive power of kernel principal components (Q2658000) (← links)
- Reproducing kernels: harmonic analysis and some of their applications (Q2659761) (← links)
- Orthogonal series density estimation and the kernel eigenvalue problem (Q2780852) (← links)
- Kernel Principal Component Analysis: Applications, Implementation and Comparison (Q2820113) (← links)
- Eigen-analysis of nonlinear PCA with polynomial kernels (Q2870762) (← links)
- Statistical Analysis and Parameter Selection for Mapper (Q4558149) (← links)
- Learning Theory (Q4680908) (← links)
- Independent Component Analysis and Blind Signal Separation (Q5898440) (← links)
- Infinite-dimensional stochastic transforms and reproducing kernel Hilbert space (Q6049820) (← links)
- Learning performance of uncentered kernel-based principal component analysis (Q6168951) (← links)
