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» What Size Test Set Gives Good Error Rate Estimates
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BMCBI
2006
110views more  BMCBI 2006»
13 years 5 months ago
Bias in error estimation when using cross-validation for model selection
Background: Cross-validation (CV) is an effective method for estimating the prediction error of a classifier. Some recent articles have proposed methods for optimizing classifiers...
Sudhir Varma, Richard Simon
ICDM
2007
IEEE
131views Data Mining» more  ICDM 2007»
13 years 9 months ago
Predicting and Optimizing Classifier Utility with the Power Law
When data collection is costly and/or takes a significant amount of time, an early prediction of the classifier performance is extremely important for the design of the data minin...
Mark Last
BMCBI
2005
153views more  BMCBI 2005»
13 years 5 months ago
A comparative review of estimates of the proportion unchanged genes and the false discovery rate
Background: In the analysis of microarray data one generally produces a vector of p-values that for each gene give the likelihood of obtaining equally strong evidence of change by...
Per Broberg
BMCBI
2008
95views more  BMCBI 2008»
13 years 5 months ago
Gene set analyses for interpreting microarray experiments on prokaryotic organisms
Background: Despite the widespread usage of DNA microarrays, questions remain about how best to interpret the wealth of gene-by-gene transcriptional levels that they measure. Rece...
Nathan L. Tintle, Aaron A. Best, Matthew DeJongh, ...
ICIP
2008
IEEE
14 years 7 months ago
Complexity modeling of spatial and temporal compensations in H.264/AVC decoding
Abstract-- Complexity modeling of spatial-temporal compensations in H.264/AVC decoding is performed by examining a rich set of inter- and intra-prediction modes. Specifically, we s...
Szu-Wei Lee, C. C. Jay Kuo