The following pages link to Learning Theory (Q5473620):
Displaying 14 items.
- Compression, inversion, and approximate PCA of dense kernel matrices at near-linear computational complexity (Q92247) (← links)
- Asymptotic error bounds for kernel-based Nyström low-rank approximation matrices (Q391804) (← links)
- A randomized algorithm for a tensor-based generalization of the singular value decomposition (Q861021) (← links)
- An efficient kernel matrix evaluation measure (Q941581) (← links)
- Dealing with large diagonals in kernel matrices (Q1881406) (← links)
- Learning in high-dimensional feature spaces using ANOVA-based fast matrix-vector multiplication (Q2087418) (← links)
- Nyström-based approximate kernel subspace learning (Q2416980) (← links)
- Fast screening framework for infection control scenario identification (Q2694059) (← links)
- Low-rank kernel learning with Bregman matrix divergences (Q2880885) (← links)
- Learning Low-Rank Kernel Matrices with Column-Based Methods (Q3590022) (← links)
- Block Basis Factorization for Scalable Kernel Evaluation (Q5203970) (← links)
- Randomized Approximation of the Gram Matrix: Exact Computation and Probabilistic Bounds (Q5251753) (← links)
- The Kernel Semi–Least Squares Method for Sparse Distance Approximation (Q5327182) (← links)
- Kernel Approximation on Algebraic Varieties (Q5886836) (← links)
