Vector-valued Reproducing Kernel Banach Spaces with Applications to Multi-task Learning
Functional Analysis
2012-02-20 v2 Machine Learning
Abstract
Motivated by multi-task machine learning with Banach spaces, we propose the notion of vector-valued reproducing kernel Banach spaces (RKBS). 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 RKBS. 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 RKBS are established.
Keywords
Cite
@article{arxiv.1111.1037,
title = {Vector-valued Reproducing Kernel Banach Spaces with Applications to Multi-task Learning},
author = {Haizhang Zhang and Jun Zhang},
journal= {arXiv preprint arXiv:1111.1037},
year = {2012}
}