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» Using Validation Sets to Avoid Overfitting in AdaBoost
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SDM
2008
SIAM
165views Data Mining» more  SDM 2008»
14 years 6 months ago
On the Dangers of Cross-Validation. An Experimental Evaluation
Cross validation allows models to be tested using the full training set by means of repeated resampling; thus, maximizing the total number of points used for testing and potential...
R. Bharat Rao, Glenn Fung
BMCBI
2004
176views more  BMCBI 2004»
14 years 4 months ago
Boosting accuracy of automated classification of fluorescence microscope images for location proteomics
Background: Detailed knowledge of the subcellular location of each expressed protein is critical to a full understanding of its function. Fluorescence microscopy, in combination w...
Kai Huang, Robert F. Murphy
ICRA
2006
IEEE
132views Robotics» more  ICRA 2006»
14 years 10 months ago
Speeding-up Multi-robot Exploration by Considering Semantic Place Information
— In this paper, we consider the problem of exploring an unknown environment with a team of mobile robots. One of the key issues in multi-robot exploration is how to assign targe...
Cyrill Stachniss, Óscar Martínez Moz...
BICOB
2009
Springer
14 years 11 months ago
A Bayesian Approach to High-Throughput Biological Model Generation
Abstract. With the availability of hundreds and soon-to-be thousands of complete genomes, the construction of genome-scale metabolic models for these organisms has attracted much a...
Xinghua Shi, Rick L. Stevens
SDM
2010
SIAM
166views Data Mining» more  SDM 2010»
14 years 6 months ago
A Permutation Approach to Validation
We give a permutation approach to validation (estimation of out-sample error). One typical use of validation is model selection. We establish the legitimacy of the proposed permut...
Malik Magdon-Ismail, Konstantin Mertsalov