Selecting model complexity in learning problems - Texas A&M University (TAMU) Scholar

To learn (or generalize) from noisy data, one must resist the temptation to pick a model for the underlying process that overfits the data. Many existing techniques solve this problem at the expense of requiring the evaluation of an absolute, a priori measure of each model's complexity. We present a method that does not. Instead, it uses a natural, relative measure of each model's complexity. This method first creates a pool of 'simple' candidate models using part of the data and then selects from among these by using the rest of the data.

Selecting model complexity in learning problems Conference Paper