Text categorization is a supervised learning process. Let D be the document collection, C be a set of categories. A trai

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Text categorization is a supervised learning process. Let D bethe document collection, C be a set of categories. A training set Tof document vector and categorization pairs is given, T={(di,c(di)) | 1 ≤ i≤m}. The naïve Bayesian algorithm needs to computethe conditional probability P(c|x), for any document X=(X1, X2, …Xn) ∊ D and for any c ∊ C. Assume that all index terms are mutuallyindependent, explain how you may be able to compute p(c|x).