Real and complex analysis, 3rd ed.
Real and complex analysis, 3rd ed.
On-line learning of smooth functions of a single variable
Theoretical Computer Science
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Support vector machines, reproducing kernel Hilbert spaces, and randomized GACV
Advances in kernel methods
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Duality and Geometry in SVM Classifiers
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
On the influence of the kernel on the consistency of support vector machines
The Journal of Machine Learning Research
A new approximate maximal margin classification algorithm
The Journal of Machine Learning Research
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Distance--Based Classification with Lipschitz Functions
The Journal of Machine Learning Research
Maximal margin classification for metric spaces
Journal of Computer and System Sciences - Special issue: Learning theory 2003
On Learning Vector-Valued Functions
Neural Computation
Feature space perspectives for learning the kernel
Machine Learning
The Journal of Machine Learning Research
The Journal of Machine Learning Research
The Journal of Machine Learning Research
Optimal learning of bandlimited functions from localized sampling
Journal of Complexity
Refinement of Reproducing Kernels
The Journal of Machine Learning Research
Just relax: convex programming methods for identifying sparse signals in noise
IEEE Transactions on Information Theory
Approximation of high-dimensional kernel matrices by multilevel circulant matrices
Journal of Complexity
Refinement of operator-valued reproducing kernels
The Journal of Machine Learning Research
Regularized learning in Banach spaces as an optimization problem: representer theorems
Journal of Global Optimization
Vector-valued reproducing kernel Banach spaces with applications to multi-task learning
Journal of Complexity
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We introduce the notion of reproducing kernel Banach spaces (RKBS) and study special semi-inner-product RKBS by making use of semi-inner-products and the duality mapping. Properties of an RKBS and its reproducing kernel are investigated. As applications, we develop in the framework of RKBS standard learning schemes including minimal norm interpolation, regularization network, support vector machines, and kernel principal component analysis. In particular, existence, uniqueness and representer theorems are established.