The following pages link to Support Vector Machines (Q3499201):
Displaying 50 items.
- Stochastic saddle-point optimization for the Wasserstein barycenter problem (Q2162697) (← links)
- Learning rate of distribution regression with dependent samples (Q2171946) (← links)
- Optimal functional supervised classification with separation condition (Q2174981) (← links)
- Post-boosting of classification boundary for imbalanced data using geometric mean (Q2179087) (← links)
- Principled analytic classifier for positive-unlabeled learning via weighted integral probability metric (Q2183591) (← links)
- Theory of deep convolutional neural networks: downsampling (Q2185717) (← links)
- Quantitative robustness of localized support vector machines (Q2191830) (← links)
- Learning rates for the kernel regularized regression with a differentiable strongly convex loss (Q2191832) (← links)
- Kernel-based maximum correntropy criterion with gradient descent method (Q2191846) (← links)
- Convergence analysis of Tikhonov regularization for non-linear statistical inverse problems (Q2192321) (← links)
- High-order sequential simulation via statistical learning in reproducing kernel Hilbert space (Q2198926) (← links)
- Robust regularized extreme learning machine for regression with non-convex loss function via DC program (Q2200186) (← links)
- Embeddings of persistence diagrams into Hilbert spaces (Q2209745) (← links)
- ERM and RERM are optimal estimators for regression problems when malicious outliers corrupt the labels (Q2209821) (← links)
- Kernel machines with missing responses (Q2209830) (← links)
- Kernel gradient descent algorithm for information theoretic learning (Q2223567) (← links)
- Reproducing kernels and choices of associated feature spaces, in the form of \(L^2\)-spaces (Q2235966) (← links)
- Forecasting bankruptcy using biclustering and neural network-based ensembles (Q2241078) (← links)
- Worst-case recovery guarantees for least squares approximation using random samples (Q2243884) (← links)
- A CUDA-based implementation of an improved SPH method on GPU (Q2244114) (← links)
- Bayesian optimization of variable-size design space problems (Q2245699) (← links)
- Efficient regularized least-squares algorithms for conditional ranking on relational data (Q2251443) (← links)
- Learning sets with separating kernels (Q2252512) (← links)
- An introduction to the Hilbert-Schmidt SVD using iterated Brownian bridge kernels (Q2256959) (← links)
- Some new bounds on the entropy numbers of diagonal operators (Q2291480) (← links)
- Convergence analysis of deterministic kernel-based quadrature rules in misspecified settings (Q2291733) (← links)
- Granularity selection for cross-validation of SVM (Q2291784) (← links)
- Robustness of learning algorithms using hinge loss with outlier indicators (Q2292231) (← links)
- Online pairwise learning algorithms with convex loss functions (Q2293252) (← links)
- Spectral asymptotics for Krein-Feller operators with respect to \(\mathrm{V} \)-variable Cantor measures (Q2300659) (← links)
- Learning with correntropy-induced losses for regression with mixture of symmetric stable noise (Q2300760) (← links)
- Optimal rates for spectral algorithms with least-squares regression over Hilbert spaces (Q2300763) (← links)
- Moving quantile regression (Q2301045) (← links)
- Random distributional response model based on spline method (Q2301099) (← links)
- On the positivity and magnitudes of Bayesian quadrature weights (Q2302459) (← links)
- Supervised learning via smoothed Polya trees (Q2303053) (← links)
- Eigendecompositions of transfer operators in reproducing kernel Hilbert spaces (Q2303767) (← links)
- Fast and strong convergence of online learning algorithms (Q2305549) (← links)
- Data driven computing with noisy material data sets (Q2310080) (← links)
- Sparse kernel deep stacking networks (Q2319474) (← links)
- Distribution-free uncertainty quantification for kernel methods by gradient perturbations (Q2320596) (← links)
- Probabilistic integration: a role in statistical computation? (Q2325605) (← links)
- Optimal rates for coefficient-based regularized regression (Q2330932) (← links)
- A hybrid DE optimized wavelet kernel SVR-based technique for algal atypical proliferation forecast in La Barca reservoir: a case study (Q2332723) (← links)
- Contingent preference disaggregation model for multiple criteria sorting problem (Q2333013) (← links)
- Convergence types and rates in generic Karhunen-Loève expansions with applications to sample path properties (Q2334556) (← links)
- Analysis of singular value thresholding algorithm for matrix completion (Q2338558) (← links)
- Generalization performance of Gaussian kernels SVMC based on Markov sampling (Q2339390) (← links)
- Learning performance of regularized moving least square regression (Q2359988) (← links)
- Stable splittings of Hilbert spaces of functions of infinitely many variables (Q2360671) (← links)
