ABSTRACT
The basic idea of the Expectation-Maximisation algorithm is that sometimes it is easier to add extra variables that are not actually known and then to maximise the function over those variables. Nearest neighbour methods can also be used for regression by returning the average value of the neighbours to a point, or a spline or similar fit as the new value. Computing the distances between all pairs of points is very computationally expensive. Nearest neighbour methods can also be used for regression by returning the average value of the neighbours to a point, or a spline or similar fit as the new value. The most common methods are known as kernel smoothers, and they use a kernel that decides how much emphasis to put onto the contribution from each datapoint according to its distance from the input.
