The Classification Problem
Statistical classification considers a set of vectors of observations x of an object or event, each of which has a known type y. This set is referred to as the training set. The problem is then to determine for a given new observation vector, what the best class should be. For a quadratic classifier, the correct solution is assumed to be quadratic in the measurements, so y will be decided based on
In the special case where each observation consists of two measurements, this means that the surfaces separating the classes will be conic sections (i.e. either a line, a circle or ellipse, a parabola or a hyperbola). In this sense we can state that a quadratic model is a generalization of the linear model, and its use is justified by the desire to extend the classifier's ability to represent more complex separating surfaces.
Read more about this topic: Quadratic Classifier
Famous quotes containing the word problem:
“It is very comforting to believe that leaders who do terrible things are, in fact, mad. That way, all we have to do is make sure we don’t put psychotics in high places and we’ve got the problem solved.”
—Tom Wolfe (b. 1931)