An overtraining-resistant stochastic modeling method for pattern recognition

A generic approach for solving problems in pattern recognition based on the synthesis of accurate multiclass discriminators from large numbers of very inaccurate weak models through the use of discrete stochastic processes is introduced.

Abstract

We will introduce a generic approach for solving problems in pattern recognition based on the synthesis of accurate multiclass discriminators from large numbers of very inaccurate "weak" models through the use of discrete stochastic processes. Contrary to the standard expectation held for the many statistical and heuristic techniques normally associated with the field, a significant feature of this method of "stochastic modeling" is its resistance to so-called "overtraining." The drop in performance of any stochastic model in going from training to test data remains comparable to that of the component weak models from which it is synthesized; and since these component models are very simple, their performance drop is small, resulting in a stochastic model whose performance drop is also small despite its high level of accuracy.