A Parzen classifier with an improved robustness against deviations between training and test data
Pattern Recognition Letters
Pattern recognition with SVM and dual-tree complex wavelets
Image and Vision Computing
Journal of Cognitive Neuroscience
Parzen windows for multi-class classification
Journal of Complexity
An effective refinement strategy for KNN text classifier
Expert Systems with Applications: An International Journal
A PSO-based weighting method for linear combination of neural networks
Computers and Electrical Engineering
An educational tool for artificial neural networks
Computers and Electrical Engineering
A comparative study of wavelet families for classification of wrist motions
Computers and Electrical Engineering
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The errors resulting from satellite configuration geometry can be determined by Geometric Dilution of Precision (GDOP). Considering optimal satellite subset selection, lower GDOP value usually causes better accuracy in GPS positioning. However, GDOP computation based on complicated transformation and inversion of measurement matrices is a time consuming procedure. This paper deals with classification of GPS GDOP utilizing Parzen estimation based Bayesian decision theory. The conditional probability of each class is estimated by Parzen algorithm. Then based on Bayesian decision theory, the class with maximum posterior probability is selected. The experiments on measured dataset demonstrate that the proposed algorithm lead, in mean classification improvement, to 4.08% in comparison with Support Vector Machine (SVM) and 9.83% in comparison with K-Nearest Neighbour (KNN) classifier. Extra work on feature extraction has been performed based on Principle Component Analysis (PCA). The results demonstrate that the feature extraction approach has best performance respect to all classifiers.