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برق و الکترونیک::
بیز ساده گاوسی
IPP Individual Prediction Performance GNB Gaussian Naïve Bayes GA Genetic Algorithm KKT Karush-Kuhn-Tucker Conditions MAR Missing At Random MC Main Classifier MCAR Missing Completely At Random NIL Non-Incremental Learning NMAR Not Missing At Random ND Novelty Detection NPP Net Prediction Performance Nr Normal NSV Number of Support Vectors NUC Non-Updated Classifier OCC One Class Classifiers OCS One Class Support Vector Machines OCS_Nr One Class Support vector machine trained with normal class OCS_F1 One Class Support vector machine trained with F1 class OCS_F2 One Class Support vector machine trained with F2 class OCS_F3 One Class Support vector machine trained with F3 class OK Ordinary Kriging OM Online Monitoring module OMU Offline Model Updating OVPP Overall Validation Prediction Performance QP Quadratic Problem RBF Radial Basis Function RMSE Root Mean Square Error RP Removing Percentage RV Retraining Value SV Support Vector
Artificial Neural Networks (ANN), Support Vector Machines (SVM), Decision Trees (DT), and Gaussian Naïve Bayes (GNB), are among most common classification methods.
1.3.1.1.3 Gaussian Naïve Bayes (GNB)
For Gaussian Naïve Bayes (GNB) classifier the Gaussian distribution is assumed.
2. 7 MC and WMC are selected among four classifiers including SVM, DT, KNN and GNB.
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