Question: machine learning Suppose you have the following training examples, described by three attributes, x 1, X2, X3, and labeled by classes c 1 and c

machine learning Suppose you have the following training examples, described by three attributes, x 1, X2, X3, and labeled by classes c 1 and machine learning

Suppose you have the following training examples, described by three attributes, x 1, X2, X3, and labeled by classes c 1 and c 2 X1 X2 X 3 Class 2.1 0.2 3.0 C1 3.3 1.0 2.9 C1 2.7 1.2. 3.4 C1 cl 0.5 5.3 0.0 C2 1.5 4.7 0.5 C2 Using these data, do the following: (a) Assuming that the attributes are mutually independent, approximate the following probability density functions: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - P(x), pc,(x), p(x). Hint: use the idea of superimposed bell functions. (b) Using the pdf's from the previous step, decide whether x = [1.4,3.3,3.0] should belong to C or C 2

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