Error estimation is fundamental in GSP applications, such as the discovery of biomarkers to classify disease, or the construction of genetic regulatory networks, especially in small sample settings. Braga-Neto and Dougherty proposed a kernel-based technique of error estimation, called bolstered error estimation, which was shown empirically to work well in low-dimensional spaces (Braga-Neto and Dougherty, 2004). We present in this paper preliminary results of a simulation study on how bolstering performs in high-dimensional spaces.
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