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Face detection has been performed using classifier and centroid calculations.
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Classification of test images has been performed using the naive Bayes classifier, which is simple, but fast and known to perform remarkably well in many practical applications with huge size database.
Based on the gene sequences, taxonomic assignments of the strains at the genus level were performed using RDP Naïve Bayesian classifier, with an 80% bootstrap cutoff ([Cole et al. 2009]).
Attribute selection was performed using the Classifier Subset Evaluator with the best first search method on the training data.
Taxonomic-based analysis was performed using the Classifier program of the Ribosomal Database Project (RDP) version 9 (http://rdp.cme.msu.edu/) to assign taxonomic status to each sequence.
This task can be performed using supervised classifiers.
For this purpose, a rigorous set of experiments are performed using various classifiers such as SVM, Decision Tree, W-J48 and KNN.
A comparison is performed using a Bayesian classifier.
The final scene classification was performed using the discriminative classifier SVM on the learned distribution obtaining 83.7% of accuracy on the same dataset used in [10].
Thus, the classification could be performed using a Bayes classifier composed of 27 hidden Markov models, each one corresponding to one class.
After coregistering and fitting the three image sequences, a multimodal tissue classification will be performed using a Bayesian classifier based on discriminant analysis.
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