I was going through this paper which proposes a new similarity metric. The evaluation is carried out using various classification and clustering techniques. I was confused about how a similarity metric is used in classification and clustering. The similarity metric may be used as a kernel in SVM along with the linear and rbf kernel. How is the metric used with naive bayes?. Is the metric used as a distance function in clustering techniques?.

As I am new to machine learning, I am confused. Can somebody clarify how are classifiers and clustering algorithms used to evaluate a similarity metric?



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