The Annals of Statistics
- Ann. Statist.
- Volume 24, Number 6 (1996), 2319-2349.
An overtraining-resistant stochastic modeling method for pattern recognition
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.
Ann. Statist., Volume 24, Number 6 (1996), 2319-2349.
First available in Project Euclid: 16 September 2002
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Kleinberg, E. M. An overtraining-resistant stochastic modeling method for pattern recognition. Ann. Statist. 24 (1996), no. 6, 2319--2349. doi:10.1214/aos/1032181157. https://projecteuclid.org/euclid.aos/1032181157