.png){kind=icon}
返回
Feature space perspectives for learning the kernel
.png){kind=icon}
OA
AI
DOI：10.1007/s10994-006-0679-0.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In this paper, we continue our study of learning an optimal kernel in a prescribed convex set of kernels (Micchelli & Pontil, 2005). We present a reformulation of this problem within a feature space environment. This leads us to study regularization in the dual space of all continuous functions on a compact domain with values in a Hilbert space with a mix norm. We also relate this problem in a special case to L-p regularization.
Keyword:
Banach space regularization
convex optimization
learning the kernels
kernel methods
sparsity
期刊
IF:
2.9
论文数:
2.7K
被引数:
3.4W
机构
暂无机构信息
引用论文
.png){kind=icon}
查看更多内容