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ICML
2000
IEEE
14 years 9 months ago
FeatureBoost: A Meta-Learning Algorithm that Improves Model Robustness
Most machine learning algorithms are lazy: they extract from the training set the minimum information needed to predict its labels. Unfortunately, this often leads to models that ...
Joseph O'Sullivan, John Langford, Rich Caruana, Av...
ETS
2009
IEEE
117views Hardware» more  ETS 2009»
13 years 6 months ago
A Two Phase Approach for Minimal Diagnostic Test Set Generation
We optimize the full-response diagnostic fault dictionary from a given test set. The smallest set of vectors is selected without loss of diagnostic resolution of the given test se...
Mohammed Ashfaq Shukoor, Vishwani D. Agrawal
PAMI
1998
87views more  PAMI 1998»
13 years 8 months ago
What Size Test Set Gives Good Error Rate Estimates?
—We address the problem of determining what size test set guarantees statistically significant results in a character recognition task, as a function of the expected error rate. ...
Isabelle Guyon, John Makhoul, Richard M. Schwartz,...
BMCBI
2011
13 years 3 months ago
Appearance frequency modulated gene set enrichment testing
Background: Gene set enrichment testing has helped bridge the gap from an individual gene to a systems biology interpretation of microarray data. Although gene sets are defined a ...
Jun Ma, Maureen A. Sartor, H. V. Jagadish
COLING
2010
13 years 3 months ago
The Bag-of-Opinions Method for Review Rating Prediction from Sparse Text Patterns
The problem addressed in this paper is to predict a user's numeric rating in a product review from the text of the review. Unigram and n-gram representations of text are comm...
Lizhen Qu, Georgiana Ifrim, Gerhard Weikum