Penalized Splines and Reproducing Kernel Methods
Penalized Splines and Reproducing Kernel Methods
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DOI：
10.1198/000313006x124541
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发表时间：
2006-08
期刊：
影响因子：
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通讯作者：
N. D. Pearce;M. Wand
中科院分区：
文献类型：
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作者：
N. D. Pearce;M. Wand
自20世纪90年代中期以来，两个数据分析研究领域-惩罚样条和再生核方法-变得非常活跃。本文展示了如何前者可以嵌入到后者通过理论再生核希尔伯特空间。这种联系促进了两个研究机构之间的相互促进。特别是，支持向量机和惩罚样条之间的连接建立。这些允许显着降低计算复杂性，并更容易纳入特殊结构，如加性。
Two data analytic research areas—penalized splines and reproducing kernel methods—have become very vibrant since the mid-1990s. This article shows how the former can be embedded in the latter via theory for reproducing kernel Hilbert spaces. This connection facilitates cross-fertilization between the two bodies of research. In particular, connections between support vector machines and penalized splines are established. These allow for significant reductions in computational complexity, and easier incorporation of special structure such as additivity.