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» Lower Bounds on the Size of Test Data Sets
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NIPS
2004
13 years 9 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
SDM
2010
SIAM
218views Data Mining» more  SDM 2010»
13 years 9 months ago
Confidence-Based Feature Acquisition to Minimize Training and Test Costs
We present Confidence-based Feature Acquisition (CFA), a novel supervised learning method for acquiring missing feature values when there is missing data at both training and test...
Marie desJardins, James MacGlashan, Kiri L. Wagsta...
BIOCOMP
2006
13 years 9 months ago
Theoretical Bounds for the Number of Inferable Edges in Sparse Random Networks
Abstract-- The inference of a network structure from experimental data providing dynamical information about the underlying system of investigation is an important and still outsta...
Frank Emmert-Streib, Matthias Dehmer
ESA
2009
Springer
149views Algorithms» more  ESA 2009»
14 years 2 months ago
On the Power of Uniform Power: Capacity of Wireless Networks with Bounded Resources
Abstract. The throughput capacity of arbitrary wireless networks under the physical Signal to Interference Plus Noise Ratio (SINR) model has received a greater deal of attention in...
Chen Avin, Zvi Lotker, Yvonne Anne Pignolet
CEC
2005
IEEE
14 years 1 months ago
On the complexity of overcoming gaps with isotropic mutations and elitist selection
We consider the (1+λ) evolution strategy, an evolutionary algorithm for minimization in Rn , using isotropic mutations. Thus, for instance, Gaussian mutations adapted by the 1/5-r...
Jens Jägersküpper