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» Introducing the User-over-Ranking Hypothesis
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COLT
1999
Springer
15 years 7 months ago
Beating the Hold-Out: Bounds for K-fold and Progressive Cross-Validation
The empirical error on a test set, the hold-out estimate, often is a more reliable estimate of generalization error than the observed error on the training set, the training estim...
Avrim Blum, Adam Kalai, John Langford
COLT
1997
Springer
15 years 7 months ago
Algorithmic Stability and Sanity-Check Bounds for Leave-one-Out Cross-Validation
: In this paper we prove sanity-check bounds for the error of the leave-one-out cross-validation estimate of the generalization error: that is, bounds showing that the worst-case e...
Michael J. Kearns, Dana Ron
ECAI
2006
Springer
15 years 7 months ago
A Real Generalization of Discrete AdaBoost
Scaling discrete AdaBoost to handle real-valued weak hypotheses has often been done under the auspices of convex optimization, but little is generally known from the original boost...
Richard Nock, Frank Nielsen
120
Voted
EVOW
2006
Springer
15 years 7 months ago
Hierarchical Clustering, Languages and Cancer
In this paper, we introduce a novel objective function for the hierarchical clustering of data from distance matrices, a very relevant task in Bioinformatics. To test the robustnes...
Pritha Mahata, Wagner Costa, Carlos Cotta, Pablo M...
113
Voted
SDM
2007
SIAM
104views Data Mining» more  SDM 2007»
15 years 4 months ago
Boosting Optimal Logical Patterns Using Noisy Data
We consider the supervised learning of a binary classifier from noisy observations. We use smooth boosting to linearly combine abstaining hypotheses, each of which maps a subcube...
Noam Goldberg, Chung-chieh Shan