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arXiv 1111.1037math.FAstat.ML
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向量值再生核巴拿赫空间及其在多任务学习中的应用

Vector-valued Reproducing Kernel Banach Spaces with Applications to Multi-task Learning

Haizhang Zhang, Jun Zhang

摘要:

受巴拿赫空间多任务机器学习的启发,我们提出了向量值再生核巴拿赫空间(RKBS)的概念。研究了空间的基本性质和相应的再生核。我们还提出了特征图的建设和向量值RKBS的几个具体的例子。然后将该理论应用于多任务机器学习。特别地,建立了向量值RKBS中正则化学习方案的极小元的表示定理和特征方程。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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