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The Ethereal
Improved Error Bounds Based on Worst Likely Assignments
March 31, 2015 ยท Declared Dead ยท ๐ IEEE International Joint Conference on Neural Network
"No code URL or promise found in abstract"
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Authors
Eric Bax
arXiv ID
1504.00052
Category
stat.ML: Machine Learning (Stat)
Cross-listed
cs.IT,
cs.LG,
math.PR
Citations
0
Venue
IEEE International Joint Conference on Neural Network
Last Checked
5 months ago
Abstract
Error bounds based on worst likely assignments use permutation tests to validate classifiers. Worst likely assignments can produce effective bounds even for data sets with 100 or fewer training examples. This paper introduces a statistic for use in the permutation tests of worst likely assignments that improves error bounds, especially for accurate classifiers, which are typically the classifiers of interest.
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