Minimum error rate detection: An adaptive bayesian approach

2017 ◽  
Vol 140 ◽  
pp. 1-11 ◽  
Author(s):  
A. Boudjellal ◽  
K. Abed-Meraim ◽  
A. Belouchrani ◽  
Ph. Ravier
Keyword(s):  
Bayesian Approach ◽  
Error Rate ◽  
Minimum Error

scholarly journals Improved Minimum Error Rate Training in Moses

2009 ◽  
Vol 91 (1) ◽  
Author(s):  
Nicola Bertoldi ◽  
Barry Haddow ◽  
Jean-Baptiste Fouet
Keyword(s):  
Error Rate ◽  
Minimum Error ◽  
Minimum Error Rate Training

Research of Soccer Robot Target Tracking Algorithm Based on Improved CAMShift

2011 ◽  
Vol 221 ◽  
pp. 610-614 ◽  
Author(s):  
Yi Hu Huang ◽  
Jin Li Wang ◽  
Xi Mei Jia
Keyword(s):  
Probability Distribution ◽  
Error Rate ◽  
Tracking Algorithm ◽  
Bayesian Decision Theory ◽  
Bayesian Decision ◽  
Soccer Robot ◽  
Color Target ◽  
Dynamic Background ◽  
Minimum Error ◽  
Search Area

According to the vision needs of robot soccer and CAMShift tracking inefficient in dynamic background, a new tracking algorithm is brought forward to improve the CAMShift in this paper. A real-time updating background model is build, by traversing the search area for all target pixels to statistic and calculate the color probability distribution of the color target, statistical principles and minimum error rate of Bayesian decision theory are used to achieve a more accurate distinction between the target and the background. By comparing with the CAMShift, the new algorithm provides a better robustness in the soccer robot game and can meet the purposes of fast and accurate tracking.

New minimum error rate algorithms for DFE

2005 ◽  
Vol 28 (3) ◽  
pp. 413-422
Author(s):  
Hong‐Yu Liu ◽  
Rainfield Yutien Yen
Keyword(s):  
Error Rate ◽  
Minimum Error

scholarly journals Detection & Classification of Lung Cancer at an Early Stage by Applying Feature Extraction-Optimization and Neural Network on Hybrid Structure

2019 ◽  
Vol 9 (2) ◽  
pp. 3737-3745
Keyword(s):  
Neural Network ◽  
Lung Cancer ◽  
Feature Extraction ◽  
Hybrid Model ◽  
Error Rate ◽  
Execution Time ◽  
Early Stage ◽  
Support Vector ◽  
Minimum Error

As of now the detection and classification of lung cancer disease is one of the most tedious tasks in the field of medical area. In the diversified sector of medical industry usage of technology plays a very important role. Detection and diagnosis of the lung cancer at an early stage with more accuracy is the most challenging task. So, in this research article 400 set of images has been used for this experiment. Best feature extraction technique and best feature optimization technique has been analyzed on the basis of parameter minimum execution time with minimum error rate. Then finest selection of features leads to an optimal classification. In this context, one of the best classification algorithm the support vector machine has been proposed in this hybrid model for the binary classification. Further Feed forward back propagation neural network has been implemented with SVM. This proposed hybrid model reduces the complexity of the system on the basis of minimum execution time that is 1.94 sec. with minimum error rate 29.25. Further better classification accuracy 99.6507% has been achieved by using this unique hybrid model

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