10 - 701 / 15 - 781 , Machine Learning : Homework 4
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چکیده
Let’s consider several Gaussian Bayes classification Models for the classification problem P (Y |X). All models here assume there are two classes (Y ∈ {0, 1}), that X is a vector of real-valued features, and P (X|Y = 1) and P (X|Y = 0) are modeled by two different Gaussian distributions. Please complete the following table by filling in the number of parameters and VC dimension of different Gaussian Bayes learning models under different settings. Each column describes a different Bayesian classification model in terms of (1) the number of features in X, (2) whether the two Gaussians for P (X|Y = 1) and P (X|Y = 0) share the same co-variance matrix, and (3) whether the model makes the naive Bayes assumption of conditional independence among features. (note the first two columns have ’-’ for Naive Bayes because these columns assume X has just one feature).
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تاریخ انتشار 2010