On-line learning of smooth functions of a single variable
Theoretical Computer Science
The nature of statistical learning theory
The nature of statistical learning theory
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
A Generalized Representer Theorem
COLT '01/EuroCOLT '01 Proceedings of the 14th Annual Conference on Computational Learning Theory and and 5th European Conference on Computational Learning Theory
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
Learning Multiple Tasks with Kernel Methods
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
Refinement of Reproducing Kernels
The Journal of Machine Learning Research
When Is There a Representer Theorem? Vector Versus Matrix Regularizers
The Journal of Machine Learning Research
Reproducing Kernel Banach Spaces for Machine Learning
The Journal of Machine Learning Research
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
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Motivated by multi-task machine learning with Banach spaces, we propose the notion of vector-valued reproducing kernel Banach spaces (RKBSs). Basic properties of the spaces and the associated reproducing kernels are investigated. We also present feature map constructions and several concrete examples of vector-valued RKBSs. The theory is then applied to multi-task machine learning. Especially, the representer theorem and characterization equations for the minimizer of regularized learning schemes in vector-valued RKBSs are established.