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» Efficient Algorithms for Minimizing Cross Validation Error
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ICML
1994
IEEE
13 years 8 months ago
Efficient Algorithms for Minimizing Cross Validation Error
Model selection is important in many areas of supervised learning. Given a dataset and a set of models for predicting with that dataset, we must choose the model which is expected...
Andrew W. Moore, Mary S. Lee
COLT
1997
Springer
13 years 9 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
PAKDD
2000
ACM
100views Data Mining» more  PAKDD 2000»
13 years 8 months ago
Discovery of Relevant Weights by Minimizing Cross-Validation Error
In order to discover relevant weights of neural networks, this paper proposes a novel method to learn a distinct squared penalty factor for each weight as a minimization problem ov...
Kazumi Saito, Ryohei Nakano
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
ECCV
2008
Springer
14 years 6 months ago
Efficient Camera Smoothing in Sequential Structure-from-Motion Using Approximate Cross-Validation
Abstract. In the sequential approach to three-dimensional reconstruction, adding prior knowledge about camera pose improves reconstruction accuracy. We add a smoothing penalty on t...
Michela Farenzena, Adrien Bartoli, Youcef Mezouar