where K is a positive definite similarity function between two conformations, often called a kernel function, which customarily takes the value 1 for identical conformations and smaller values for pairs of conformations that are less similar to one another, and x are the unknown coefficients. The sum ranges over a set M of representative conformations. For finite M , the basis is not complete, but by choosing the set appropriately (typically by drawing conformations from the same or a related distribution corresponding to where we expect to evaluate the function) the basis set is made relevant, and by enlarging the representative set, the approximation error can be decreased. This manner in which the basis set is adapted to the data is the principal way by which the problem of high dimensionality is circumvented. The success of this type of fitting then depends entirely on the regularity properties (colloquially, smoothness) of the target function.