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Integration of prior knowledge of measurement noise in kernel density classification
- Source :
-
Pattern Recognition . Jan2008, Vol. 41 Issue 1, p320-330. 11p. - Publication Year :
- 2008
-
Abstract
- Abstract: 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. [Copyright &y& Elsevier]
Details
- Language :
- English
- ISSN :
- 00313203
- Volume :
- 41
- Issue :
- 1
- Database :
- Academic Search Index
- Journal :
- Pattern Recognition
- Publication Type :
- Academic Journal
- Accession number :
- 26334669
- Full Text :
- https://doi.org/10.1016/j.patcog.2007.05.005