Preliminary study on bolstered error estimation in high-dimensional spaces Conference Paper uri icon

abstract

  • 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. 2008 IEEE.

name of conference

  • 2008 IEEE International Workshop on Genomic Signal Processing and Statistics

published proceedings

  • 2008 IEEE INTERNATIONAL WORKSHOP ON GENOMIC SIGNAL PROCESSING AND STATISTICS

author list (cited authors)

  • Vu, T. T., Braga-Neto, U., & Dougherty, E. R.

citation count

  • 2

complete list of authors

  • Vu, TT||Braga-Neto, U||Dougherty, ER

publication date

  • January 2008