The following pages link to Support Vector Machines (Q3499201):
Displaying 50 items.
- Sequential model based optimization of partially defined functions under unknown constraints (Q2022235) (← links)
- Dimensionality reduction of complex metastable systems via kernel embeddings of transition manifolds (Q2022651) (← links)
- A novel semi-supervised support vector machine with asymmetric squared loss (Q2036148) (← links)
- Distributed regularized least squares with flexible Gaussian kernels (Q2036424) (← links)
- Kernel machines for current status data (Q2051247) (← links)
- Analysis of regularized least-squares in reproducing kernel Kreĭn spaces (Q2051308) (← links)
- A statistical learning assessment of Huber regression (Q2054280) (← links)
- Fast generalization error bound of deep learning without scale invariance of activation functions (Q2055056) (← links)
- A hybrid acceleration strategy for nonparallel support vector machine (Q2055553) (← links)
- Convergence rate estimates for the kernelized predictor corrector method for fractional order initial value problems (Q2059247) (← links)
- \(L_2\)-norm sampling discretization and recovery of functions from RKHS with finite trace (Q2059812) (← links)
- Bayesian feature interaction selection for factorization machines (Q2060718) (← links)
- Dynamic mode decomposition for continuous time systems with the Liouville operator (Q2062874) (← links)
- Support vector machine classifiers by non-Euclidean margins (Q2063337) (← links)
- Training image free high-order stochastic simulation based on aggregated kernel statistics (Q2066809) (← links)
- Alpha Procrustes metrics between positive definite operators: a unifying formulation for the Bures-Wasserstein and Log-Euclidean/Log-Hilbert-Schmidt metrics (Q2069860) (← links)
- Adaptive learning rates for support vector machines working on data with low intrinsic dimension (Q2073699) (← links)
- Geometric insights into support vector machine behavior using the KKT conditions (Q2074327) (← links)
- Convergence rates of support vector machines regression for functional data (Q2074933) (← links)
- Error bounds of the invariant statistics in machine learning of ergodic Itô diffusions (Q2077623) (← links)
- Kernel-based prediction of non-Markovian time series (Q2077859) (← links)
- An elementary analysis of ridge regression with random design (Q2080945) (← links)
- Classification of events using local pair correlation functions for spatial point patterns (Q2084443) (← links)
- Testing subspace restrictions in the presence of high dimensional nuisance parameters (Q2084475) (← links)
- Safe trajectory tracking for underactuated vehicles with partially unknown dynamics (Q2086008) (← links)
- On the speed of uniform convergence in Mercer's theorem (Q2091033) (← links)
- High-probability stable Gaussian process-supported model predictive control for Lur'e systems (Q2095350) (← links)
- Understanding neural networks with reproducing kernel Banach spaces (Q2105111) (← links)
- Approximate kernel PCA: computational versus statistical trade-off (Q2105193) (← links)
- A sieve stochastic gradient descent estimator for online nonparametric regression in Sobolev ellipsoids (Q2105198) (← links)
- Hierarchical identification of nonlinear hybrid systems in a Bayesian framework (Q2105431) (← links)
- Over-the-Air computation for distributed machine learning and consensus in large wireless networks (Q2106494) (← links)
- From inexact optimization to learning via gradient concentration (Q2111477) (← links)
- Do ideas have shape? Idea registration as the continuous limit of artificial neural networks (Q2111734) (← links)
- Batch policy learning in average reward Markov decision processes (Q2112817) (← links)
- Quantum Jensen-Shannon divergences between infinite-dimensional positive definite operators (Q2117844) (← links)
- Machine learning with kernels for portfolio valuation and risk management (Q2120539) (← links)
- Concentration inequalities for cross-validation in scattered data approximation (Q2120815) (← links)
- Online gradient descent algorithms for functional data learning (Q2121498) (← links)
- State-based confidence bounds for data-driven stochastic reachability using Hilbert space embeddings (Q2123214) (← links)
- Operator-theoretic framework for forecasting nonlinear time series with kernel analog techniques (Q2125604) (← links)
- Functional sufficient dimension reduction through average Fréchet derivatives (Q2131260) (← links)
- Countable tensor products of Hermite spaces and spaces of Gaussian kernels (Q2136858) (← links)
- On Gaussian kernels on Hilbert spaces and kernels on hyperbolic spaces (Q2139169) (← links)
- A new upper bound for sampling numbers (Q2143216) (← links)
- Multi-output support vector frontiers (Q2147031) (← links)
- Quantum-hybrid neural vector quantization -- a mathematical approach (Q2148712) (← links)
- Lower bounds for invariant statistical models with applications to principal component analysis (Q2157446) (← links)
- Support vector regression with penalized likelihood (Q2157521) (← links)
- Learning rates of kernel-based robust classification (Q2157879) (← links)
