## create a simple GUI-based program called ChooseSaveFile.java that has no components, but creates an instance of itself within main and has the usual window-closing code (also within main).

Using class Personnel from Section 4.9, create a simple GUI-based program

called ChooseSaveFile.java that has no components, but creates an instance of

itself within main and has the usual window-closing code (also within main).

Within the constructor for the class, declare and initialise an array of three

Personnel objects (as in program VectorSerialise.java from Section 4.9) and

write the objects from the array to a file (using an ObjectOutputStream). The

name and location of the file should be chosen by the user via a JFileChooser

object. Note that you will need to close down the (empty) application window

by clicking on the window close box.

### Compute the information gain of the term “elections” according to Eq. 5.7.

Consider the term “elections” which is present in only 50 documents in a corpus of 1000 documents. Furthermore, assume that the corpus contains 100 documents belonging to the Politics category,….

### 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….

### What are the possible advantages and disadvantages of using such an approach?

1.       Na¨ıve Bayes is a generative model in which each class corresponds to one mixture component. Design a semi-supervised generalization of the nai¨ıve Bayes model in which each of the….