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ICML
2001
IEEE
14 years 5 months ago
An Improved Predictive Accuracy Bound for Averaging Classifiers
We present an improved bound on the difference between training and test errors for voting classifiers. This improved averaging bound provides a theoretical justification for popu...
John Langford, Matthias Seeger, Nimrod Megiddo
ICML
2002
IEEE
14 years 5 months ago
Exact model averaging with naive Bayesian classifiers
The naive classifier is a well-established mathematical model whose simplicity, speed and accuracy have made it a popular choice for classification in AI and engineering. In this ...
Denver Dash, Gregory F. Cooper
BMCBI
2004
176views more  BMCBI 2004»
13 years 4 months ago
Boosting accuracy of automated classification of fluorescence microscope images for location proteomics
Background: Detailed knowledge of the subcellular location of each expressed protein is critical to a full understanding of its function. Fluorescence microscopy, in combination w...
Kai Huang, Robert F. Murphy
ICDM
2003
IEEE
220views Data Mining» more  ICDM 2003»
13 years 10 months ago
Exploiting Unlabeled Data for Improving Accuracy of Predictive Data Mining
Predictive data mining typically relies on labeled data without exploiting a much larger amount of available unlabeled data. The goal of this paper is to show that using unlabeled...
Kang Peng, Slobodan Vucetic, Bo Han, Hongbo Xie, Z...
PLDI
2005
ACM
13 years 10 months ago
Code placement for improving dynamic branch prediction accuracy
Code placement techniques have traditionally improved instruction fetch bandwidth by increasing instruction locality and decreasing the number of taken branches. However, traditio...
Daniel A. Jiménez