Predict the probabilities of categories Cat and Car of Test2 on the toy corpus example in Sect. 5.3.5.2. You can use the multinomial na¨ıve Bayes model with the same level of smoothing as used in the example in the book. Return normalized probabilities that sum to 1 over the two categories. 2.       Na¨ıve Bayes is a generative model in which each class corresponds to one mixture component. Design a fully supervised generalization of the nai¨ıve Bayes model in which each of the k classes contains exactly b > 1 mixture components for a total of b · k mixture components. How would you perform parameter estimation in this model?

1.       Predict the probabilities of categories Cat and Car of Test2 on the toy corpus example in Sect. 5.3.5.2. You can use the multinomial na¨ıve Bayes model with the same level of smoothing as used in the example in the book. Return normalized probabilities that sum to 1 over the two categories.

2.       Na¨ıve Bayes is a generative model in which each class corresponds to one mixture component. Design a fully supervised generalization of the nai¨ıve Bayes model in which each of the k classes contains exactly b > 1 mixture components for a total of b · k mixture components. How would you perform parameter estimation in this model?

find the cost of your paper

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