Chapter 7
Probabilistic Learning
One criticism that is often made of neural networks—especially the MLP—is that it is not clear exactly what it is doing: while we can go and have a look at the activations of the neurons and the weights, they don’t tell us much. We’ve already seen some methods that don’t have this problem, principally the decision tree in Chapter 12. In this chapter we are going to look at methods that are based on statistics, and that are therefore more transparent, in that we can always extract and look at the probabilities and see what they are, rather than having to worry about weights that have no obvious meaning.
We will look at how to perform classification by using the frequency with which examples appear in the training ...
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