The title of this section is a bit of inside jargon, which you will now learn. Starting in the 1990s, first in the biomedical domain and then on the Web, problems started to appear when P was greater than N. What this means is that the number of features, P, was greater than the number of examples, N (these letters were the conventional statistical shorthand for these concepts). These became known as "P greater than N" problems.
For example, if your input is a set of written text, a simple way to approach it is to consider each possible word in the dictionary as a feature and regress on those (we will later work on one such problem ourselves). In the English language, you have over 20,000 words (this is if you perform some stemming and only consider common words; it is more than ten times that if you keep trademarks). If you only have a few hundred or a few thousand examples, you will have more features than examples.
In this case, as the number of features is greater...
