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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 9 days 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»
13 years 10 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 4 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 5 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 10 days 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