The following pages link to Learning Theory (Q3426914):
Displaying 50 items.
- Adaptive kernel methods using the balancing principle (Q1959089) (← links)
- The generalization performance of ERM algorithm with strongly mixing observations (Q1959486) (← links)
- Robust pairwise learning with Huber loss (Q1979426) (← links)
- Kernel conjugate gradient methods with random projections (Q1979923) (← links)
- Optimal learning with anisotropic Gaussian SVMs (Q1979927) (← links)
- A closer look at covering number bounds for Gaussian kernels (Q1996885) (← links)
- Multi-task learning in vector-valued reproducing kernel Banach spaces with the \(\ell^1\) norm (Q1996886) (← links)
- Coefficient-based regression with non-identical unbounded sampling (Q2016624) (← links)
- Asymptotic expansion for neural network operators of the Kantorovich type and high order of approximation (Q2023320) (← links)
- Numerical solution of the parametric diffusion equation by deep neural networks (Q2049099) (← links)
- Analysis of regularized least-squares in reproducing kernel Kreĭn spaces (Q2051308) (← links)
- A promenade through correct test sequences. I: Degree of constructible sets, Bézout's inequality and density (Q2052162) (← links)
- A statistical learning assessment of Huber regression (Q2054280) (← links)
- A direct approach for function approximation on data defined manifolds (Q2057766) (← links)
- Optimal stochastic Bernstein polynomials in Ditzian-Totik type modulus of smoothness (Q2059641) (← links)
- \(L_2\)-norm sampling discretization and recovery of functions from RKHS with finite trace (Q2059812) (← links)
- Random sampling and approximation of signals with bounded derivatives (Q2067835) (← links)
- Superquantiles at work: machine learning applications and efficient subgradient computation (Q2070410) (← links)
- Generalized Dobrushin ergodicity coefficient and ergodicities of non-homogeneous Markov chains (Q2073159) (← links)
- A statistical learning perspective on switched linear system identification (Q2081825) (← links)
- On the speed of uniform convergence in Mercer's theorem (Q2091033) (← links)
- Stochastic quasi-interpolation with Bernstein polynomials (Q2094526) (← links)
- Sharp estimates for the covering numbers of the Weierstrass fractal kernel (Q2099268) (← links)
- Functional linear regression with Huber loss (Q2099272) (← links)
- Machine learning with kernels for portfolio valuation and risk management (Q2120539) (← links)
- Online gradient descent algorithms for functional data learning (Q2121498) (← links)
- Multivariate weighted Kantorovich operators (Q2124204) (← links)
- Operator-theoretic framework for forecasting nonlinear time series with kernel analog techniques (Q2125604) (← links)
- Approximations of conditional probability density functions in Lebesgue spaces via mixture of experts models (Q2129248) (← links)
- On Gaussian kernels on Hilbert spaces and kernels on hyperbolic spaces (Q2139169) (← links)
- Divergence-free quasi-interpolation (Q2155816) (← links)
- Learning interaction kernels in stochastic systems of interacting particles from multiple trajectories (Q2162118) (← links)
- Fast rates of minimum error entropy with heavy-tailed noise (Q2168008) (← links)
- Learning rate of distribution regression with dependent samples (Q2171946) (← links)
- Distributed kernel gradient descent algorithm for minimum error entropy principle (Q2175022) (← links)
- A new randomized Kaczmarz based kernel canonical correlation analysis algorithm with applications to information retrieval (Q2179300) (← links)
- Echo state networks are universal (Q2182904) (← links)
- Random sampling in reproducing kernel subspaces of \(L^p(\mathbb{R}^n)\) (Q2195184) (← links)
- Optimal classification of Gaussian processes in homo- and heteroscedastic settings (Q2195853) (← links)
- Local RBF-based penalized least-squares approximation on the sphere with noisy scattered data (Q2196025) (← links)
- Partial multi-dividing ontology learning algorithm (Q2200577) (← links)
- Convolution random sampling in multiply generated shift-invariant spaces of \(L^p(\mathbb{R}^d)\) (Q2218256) (← links)
- Random sampling in multiply generated shift-invariant subspaces of mixed Lebesgue spaces \(L^{p,q}(\mathbb{R}\times\mathbb{R}^d)\) (Q2222181) (← links)
- Reproducing kernels and choices of associated feature spaces, in the form of \(L^2\)-spaces (Q2235966) (← links)
- Learning performance of regularized regression with multiscale kernels based on Markov observations (Q2244161) (← links)
- Convergence rates of learning algorithms by random projection (Q2252501) (← links)
- On empirical eigenfunction-based ranking with \(\ell^1\) norm regularization (Q2256621) (← links)
- Mathematics of the neural response (Q2269906) (← links)
- Optimal learning rates for distribution regression (Q2283125) (← links)
- Some new bounds on the entropy numbers of diagonal operators (Q2291480) (← links)
