Smooth Bayesian kernel machines

被引：0
|
作者：
ter Borg, RW
Rothkrantz, LJM
机构：
[1] Nuon NV, NL-1096 BA Amsterdam, Netherlands
[2] Delft Univ Technol, NL-2628 CD Delft, Netherlands
来源：
ARTIFICIAL NEURAL NETWORKS: FORMAL MODELS AND THEIR APPLICATIONS - ICANN 2005, PT 2, PROCEEDINGS | 2005年 / 3697卷
关键词：
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper, we consider the possibility of obtaining a kernel machine that is sparse in feature space and smooth in output space. Smooth in output space implies that the underlying function is supposed to have continuous derivatives up to some order. Smoothness is achieved by applying a roughness penalty, a concept from the area of functional data analysis. Sparseness is taken care of by automatic relevance determination. Both axe combined in a Bayesian model, which has been implemented and tested. Test results axe presented in the paper.
引用
收藏
页码：577 / 582
页数：6
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