I'm currently trying to decide what classifier to use for my snore detection algorithm. Which uses some features like Zero cross rating, frequency band energy, first formants...

The classifiers which have been pointed to are:

Support Vector Machine Naive Bayes Classifier Knn Nearest Neighbour Linear or Quadratic Classifier

However, could somebody describe what situation each classifier is used in, or point me to a good paper, website for article which describes each classifier well.. I find the Mathworks website hard to understand the difference between each.



1 Answer 1


If you don't know what you're doing, naive bayes is hard to screw up. The naive part of naive bayes is that it assumes that all of your features are independent. They probably aren't independent but in many cases it doesn't matter so much that this assumption is violated. Naive bayes is also good at handling different feature types such as using both categorical variables and continuous variables for the same classification task.


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