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I am using a NormalBayesClassifier trained on PyramidGFTT features and SIFT descriptors. I have 8 classes, each contain a number of images (eg: 423, 1230, 826, etc). I have trained the classifier on a random segment of 400 images from each class, and then test it on the rest. What I want to ask is, because I have a class that is the smallest (423 images), does this have any influence if I am training the classifier on 80% of images of each class? Can this lead to wrong prediction? I am asking this, because the SVM classifier is dependent on the number of images in each class.

I am using a NormalBayesClassifier trained on PyramidGFTT features and SIFT descriptors. I have 8 classes, each contain a number of images (eg: 423, 1230, 826, etc). I have trained the classifier on a random segment of 400 images from each class, and then test it on the rest. What I want to ask is, because I have a class that is the smallest (423 images), does this have any influence if I am training the classifier on 80% of images of each class? Can this lead to wrong prediction? I am asking this, because I know that the SVM classifier is dependent on the number of images in each class.

I am using a NormalBayesClassifier trained on PyramidGFTT features and SIFT descriptors. I have 8 classes, each contain a number of images (eg: 423, 1230, 826, etc). I have trained the classifier on a random segment of 400 images from each class, and then test it on the rest. What I want to ask is, because I have a class that is the smallest (423 images), does this have any influence if I am training the classifier on 80% of images of each class? Can this lead to wrong prediction? I am asking this, because I know that the SVM classifier is dependent on the number of images in each class.class and I am wondering if NormalBayesClassifier is the same.