Confidence bands for least squares support vector machine classifiers: A regression approach

被引：17
|
作者：
De Brabanter, K. [1 ]
Karsmakers, P. [1 ,2 ]
De Brabanter, J. [1 ,3 ,4 ]
Suykens, J. A. K. [1 ,4 ]
De Moor, B. [1 ,4 ]
机构：
[1] Katholieke Univ Leuven, Dept Elect Engn ESAT SCD, B-3001 Louvain, Belgium
[2] KH Kempen Associatie KU Leuven, Dept IBW, B-2440 Geel, Belgium
[3] Katholieke Univ Leuven, Hogesch KaHo Sint Lieven, Dept E&A 11, B-9000 Ghent, Belgium
[4] IBBT KU Leuven Future Hlth Dept, B-3001 Louvain, Belgium
来源：
PATTERN RECOGNITION | 2012年 / 45卷 / 06期
关键词：
Kernel based classification; Bias; Variance; Linear smoother; Higher-order kernel; Simultaneous confidence intervals;
D O I：
10.1016/j.patcog.2011.11.021
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This paper presents bias-corrected 100(1-alpha)% simultaneous confidence bands for least squares support vector machine classifiers based on a regression framework. The bias, which is inherently present in every nonparametric method, is estimated using double smoothing. In order to obtain simultaneous confidence bands we make use of the volume-of-tube formula. We also provide extensions of this formula in higher dimensions and show that the width of the bands are expanding with increasing dimensionality. Simulations and data analysis support its usefulness in practical real life classification problems. (C) 2011 Elsevier Ltd. All rights reserved.
引用
收藏
页码：2280 / 2287
页数：8
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