کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
532797 | 869994 | 2008 | 11 صفحه PDF | دانلود رایگان |
![عکس صفحه اول مقاله: Integration of prior knowledge of measurement noise in kernel density classification Integration of prior knowledge of measurement noise in kernel density classification](/preview/png/532797.png)
Samples can be measured with different precisions and reliabilities in different experiments, or even within the same experiment. These varying levels of measurement noise may deteriorate the performance of a pattern recognition system, if not treated with care. Here we seek to investigate the benefit of incorporating prior knowledge about measurement noise into system construction. We propose a kernel density classifier which integrates such prior knowledge. Instead of using an identical kernel for each sample, we transform the prior knowledge into a distinct kernel for each sample. The integration procedure is straightforward and easy to interpret. In addition, we show how to estimate the diverse measurement noise levels in a real world dataset. Compared to the basic methods, the new kernel density classifier can give a significantly better classification performance. As expected, this improvement is more obvious for small sample size datasets and large number of features.
Journal: Pattern Recognition - Volume 41, Issue 1, January 2008, Pages 320–330