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JMLR
2008
104views more  JMLR 2008»
13 years 4 months ago
Nearly Uniform Validation Improves Compression-Based Error Bounds
This paper develops bounds on out-of-sample error rates for support vector machines (SVMs). The bounds are based on the numbers of support vectors in the SVMs rather than on VC di...
Eric Bax
FUIN
2006
95views more  FUIN 2006»
13 years 4 months ago
Bounds for Validation
In this paper we derive the bounds for Validation (known also as Hold-Out Estimate and Train-and-Test Method). We present the best possible bound in the case of 0-1 valued loss fun...
Wojciech Jaworski
NIPS
2004
13 years 6 months ago
Co-Validation: Using Model Disagreement on Unlabeled Data to Validate Classification Algorithms
In the context of binary classification, we define disagreement as a measure of how often two independently-trained models differ in their classification of unlabeled data. We exp...
Omid Madani, David M. Pennock, Gary William Flake
COLT
1997
Springer
13 years 8 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
COLT
1999
Springer
13 years 9 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