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NPL
2006
109views more  NPL 2006»
13 years 7 months ago
CB3: An Adaptive Error Function for Backpropagation Training
Effective backpropagation training of multi-layer perceptrons depends on the incorporation of an appropriate error or objective function. Classification-based (CB) error functions ...
Michael Rimer, Tony Martinez
CHI
2009
ACM
14 years 8 months ago
Understanding teamwork in high-risk domains through analysis of errors
Trauma care is an example of dynamic, complex, and safety-critical teamwork. The staff in trauma centers works under time pressure and lacks effective information technologies to ...
Aleksandra Sarcevic
DIS
2006
Springer
13 years 11 months ago
Incremental Algorithm Driven by Error Margins
Incremental learning is an approach to deal with the classification task when datasets are too large or when new examples can arrive at any time. One possible approach uses concent...
Gonzalo Ramos-Jiménez, José del Camp...
MVA
1990
13 years 8 months ago
A Pattern Classifier Integrating Multilayer Perceptron and Error-Correcting Code
In this paper we present a novel classifier which integrates a multilayer perceptron and a error-correcting decoder. There are two stages in the classifier,in the first stage, map...
Haibo Li, Torbjörn Kronander, Ingemar Ingemar...
NIPS
2004
13 years 8 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