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Vector-valued reproducing kernel Banach spaces with applications to multi-task learning

Haizhang Zhang School of Mathematics and Computational Science, Sun Yat-sen University Jun Zhang Department of Psychology, University of Michigan

mathscidoc:1609.01002

Journal of Complexity, 29, (2), 195–215, 2013
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.
Characterization equations / Feature maps / Regularized learning / The representer theorem / Vector-valued reproducing kernel Banach spaces
[ Download ] [ 2016-09-20 18:09:02 uploaded by zhang ] [ 676 downloads ] [ 0 comments ] [ Cited by 1 ] 
@inproceedings{haizhang2013vector-valued,
  title={Vector-valued reproducing kernel Banach spaces with applications to multi-task learning},
  author={Haizhang Zhang, and Jun Zhang},
  url={http://archive.ymsc.tsinghua.edu.cn/pacm_paperurl/20160920180902753598028},
  booktitle={Journal of Complexity},
  volume={29},
  number={2},
  pages={195–215},
  year={2013},
}
Haizhang Zhang, and Jun Zhang. Vector-valued reproducing kernel Banach spaces with applications to multi-task learning. 2013. Vol. 29. In Journal of Complexity. pp.195–215. http://archive.ymsc.tsinghua.edu.cn/pacm_paperurl/20160920180902753598028.
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