scientific article
From MaRDI portal
zbMath0992.68088MaRDI QIDQ3148817
Bernhard Schölkopf, Ralf Herbrich, Alexander J. Smola
Publication date: 22 September 2002
Full work available at URL: http://link.springer.de/link/service/series/0558/bibs/2111/21110416
Title: zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Related Items
Penalized Projected Kernel Calibration for Computer Models, Kernel partial correlation: a novel approach to capturing conditional independence in graphical models for noisy data, Boltzmann–Gibbs Random Fields with Mesh-free Precision Operators Based on Smoothed Particle Hydrodynamics, Parameter choices for sparse regularization with the ℓ1 norm *, Unnamed Item, Semi-Infinite Linear Regression and Its Applications, What Kinds of Functions Do Deep Neural Networks Learn? Insights from Variational Spline Theory, Unnamed Item, A scalable approximate Bayesian inference for high-dimensional Gaussian processes, An Online Projection Estimator for Nonparametric Regression in Reproducing Kernel Hilbert Spaces, A multi-birth metric learning framework based on binary constraints, On the Inconsistency of Kernel Ridgeless Regression in Fixed Dimensions, A nonlinear kernel SVM Classifier via \(L_{0/1}\) soft-margin loss with classification performance, Superoscillations and Fock spaces, Reproducing kernel Hilbert spaces in the mean field limit, Sparse machine learning in Banach spaces, A Reproducing Kernel Hilbert Space Approach to Functional Calibration of Computer Models, A new classifier for imbalanced data based on a generalized density ratio model, Unnamed Item, Unnamed Item, Accelerating metabolic models evaluation with statistical metamodels: application to Salmonella infection models, Domain Generalization by Functional Regression, Unified SVM algorithm based on LS-DC loss, Learning system parameters from Turing patterns, Linear inverse problems with Hessian-Schatten total variation, A duality approach to regularized learning problems in Banach spaces, Kernel-based identification with frequency domain side-information, The Geometry of Sparse Analysis Regularization, Measuring Complexity of Learning Schemes Using Hessian-Schatten Total Variation, Regularization method for the generalized moment problem in a functional reproducing kernel Hilbert space, Quantitative robustness of instance ranking problems, Hilbert C∗-Module for Analyzing Structured Data, From statistical to causal learning, A novel identification scheme of an inverse source problem based on Hilbert reproducing kernels, Kernel methods are competitive for operator learning, On Kernel Method–Based Connectionist Models and Supervised Deep Learning Without Backpropagation, Robust Support Vector Machines for Classification with Nonconvex and Smooth Losses, Generalized Mercer Kernels and Reproducing Kernel Banach Spaces, Convergence of Gaussian Process Regression with Estimated Hyper-Parameters and Applications in Bayesian Inverse Problems, A Statistical Method for Emulation of Computer Models With Invariance-Preserving Properties, With Application to Structural Energy Prediction, On the Improved Rates of Convergence for Matérn-Type Kernel Ridge Regression with Application to Calibration of Computer Models, Data-Driven Modeling for Wave-Propagation, Unnamed Item, Unnamed Item, Unnamed Item, Unnamed Item, Nyström subsampling method for coefficient-based regularized regression, Generalized support vector regression: Duality and tensor-kernel representation, On Representer Theorems and Convex Regularization, Semi-parametric adjustment to computer models, Adjustments to Computer Models via Projected Kernel Calibration, Kernel partial least squares for stationary data, Multikernel Regression with Sparsity Constraint, Linearly Constrained Linear Quadratic Regulator from the Viewpoint of Kernel Methods, Unnamed Item, Unnamed Item, Frames, their relatives and reproducing kernel Hilbert spaces, Kernel and Dissimilarity Methods for Exploratory Analysis in a Social Context, Learning performance of regularized moving least square regression, Fast rates by transferring from auxiliary hypotheses, Apportioned margin approach for cost sensitive large margin classifiers, A review on instance ranking problems in statistical learning, Generalized vec trick for fast learning of pairwise kernel models, Statistical properties of the method of regularization with periodic Gaussian reproducing kernel, Tikhonov, Ivanov and Morozov regularization for support vector machine learning, Support vector machines maximizing geometric margins for multi-class classification, Principal manifold learning by sparse grids, Consistent identification of Wiener systems: a machine learning viewpoint, Double linear regressions for single labeled image per person face recognition, \textsf{StreaMRAK} a streaming multi-resolution adaptive kernel algorithm, Generalized semi-inner products with applications to regularized learning, A study on three linear discriminant analysis based methods in small sample size problem, Terminated Ramp--Support Vector machines: A nonparametric data dependent kernel, Neural kernels for recursive support vector regression as a model for episodic memory, Nonlinear and additive principal component analysis for functional data, Evaluation of statistical relationship of random variables via mutual information, A kernel learning framework for domain adaptation learning, Learning rates of regularized regression on the unit sphere, Fisher-regularized support vector machine, A nonlinear data-driven reduced order model for computational homogenization with physics/pattern-guided sampling, Kernel-based methods for Volterra series identification, Deterministic error bounds for kernel-based learning techniques under bounded noise, Convex optimization in sums of Banach spaces, Handling concept drift via model reuse, Functional reproducing kernel Hilbert spaces for non-point-evaluation functional data, On reproducing kernel Banach spaces: generic definitions and unified framework of constructions, On asymptotic properties of Bayesian partially linear models, A Riemann-Stein kernel method, Efficient spatio-temporal Gaussian regression via Kalman filtering, On the mathematical foundations of stable RKHSs, Efficient calibration for imperfect computer models, Regularized learning schemes in feature Banach spaces, When is there a representer theorem? Nondifferentiable regularisers and Banach spaces, Non-negativity constraints on the pre-image for pattern recognition with kernel machines, Multivariate spline analysis for multiplicative models: estimation, testing and application to climate change, Iterative category discovery via multiple kernel metric learning, Variational Gram Functions: Convex Analysis and Optimization, An efficient method for clustered multi-metric learning, On minimum distribution discrepancy support vector machine for domain adaptation, Efficient cross-validation for kernelized least-squares regression with sparse basis expansions, Effective dimensionality reduction using kernel locality preserving partial least squares discriminant analysis, Dynamical memory control based on projection technique for online regression, Support vector machines with applications, Regularized least-squares regression: learning from a sequence, The theory of the quantum kernel-based binary classifier, Quantum-enhanced least-square support vector machine: simplified quantum algorithm and sparse solutions, On structured output training: hard cases and an efficient alternative, Methods for the combination of kernel matrices within a support vector framework, An efficient algorithm for learning to rank from preference graphs, A kernel-based framework to tensorial data analysis, Kernel methods in system identification, machine learning and function estimation: a survey, Kernelization of matrix updates, when and how?, A linear recurrent kernel online learning algorithm with sparse updates, A novel supervised dimensionality reduction algorithm: graph-based Fisher analysis, A unified framework for semi-supervised dimensionality reduction, A unifying representer theorem for inverse problems and machine learning, Regularization in a functional reproducing kernel Hilbert space, Convergence rates for a class of estimators based on Stein's method, Bounding the difference between RankRC and RankSVM and application to multi-level rare class kernel ranking, Kernel variable selection for multicategory support vector machines, A tutorial on kernel methods for categorization, Consistent learning by composite proximal thresholding, A statistical pipeline for identifying physical features that differentiate classes of 3D shapes, Discriminatively regularized least-squares classification, Application of mutual information estimation for predicting the structural stability of pentapeptides, Multi-task learning in vector-valued reproducing kernel Banach spaces with the \(\ell^1\) norm, Support vector machine with manifold regularization and partially labeling privacy protection, On-line independent support vector machines, Regularized learning in Banach spaces as an optimization problem: representer theorems, The kernel Kalman rule. Efficient nonparametric inference by recursive least-squares and subspace projections, Tight frame expansions of multiscale reproducing kernels in Sobolev spaces, Learning with generalization capability by kernel methods of bounded complexity, The weight-decay technique in learning from data: an optimization point of view, Quantification and compensation of thermal distortion in additive manufacturing: a computational statistics approach, Approximate normalized cuts without eigen-decomposition, Evolutionary combination of kernels for nonlinear feature transformation, System identification using kernel-based regularization: new insights on stability and consistency issues, When is there a representer theorem? Reflexive Banach spaces, Efficient approximate leave-one-out cross-validation for kernel logistic regression, An algebraic characterization of the optimum of regularized kernel methods, 9 Kernel methods for surrogate modeling, Sparse RKHS estimation via globally convex optimization and its application in LPV-IO identification, Bayesian numerical methods for nonlinear partial differential equations, Variable prioritization in nonlinear black box methods: a genetic association case study, Distribution-free uncertainty quantification for kernel methods by gradient perturbations, Sparsest piecewise-linear regression of one-dimensional data, Robustness by reweighting for kernel estimators: an overview, Functional estimation of anisotropic covariance and autocovariance operators on the sphere, Testing subspace restrictions in the presence of high dimensional nuisance parameters, Boosting as a kernel-based method, Robustness of reweighted least squares kernel based regression, Bayesian Approximate Kernel Regression With Variable Selection, Unnamed Item, Learning using privileged information: SVM+ and weighted SVM, Unnamed Item, Large scale tensor regression using kernels and variational inference, Understanding neural networks with reproducing kernel Banach spaces, Premise selection for mathematics by corpus analysis and kernel methods
