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MCS
2009
Springer

Improved Uniformity Enforcement in Stochastic Discrimination

9 years 5 months ago
Improved Uniformity Enforcement in Stochastic Discrimination
There are a variety of methods for inducing predictive systems from observed data. Many of these methods fall into the field of study of machine learning. Some of the most effective algorithms in this domain succeed by combining a number of distinct predictive elements to form what can be described as a type of committee. Well known examples of such algorithms are AdaBoost, bagging and random forests. Stochastic discrimination is a committee-forming algorithm that attempts to combine a large number of relatively simple predictive elements in an effort to achieve a high degree of accuracy. A key element of the success of this technique is that its coverage of the observed feature space should be uniform in nature. We introduce a new uniformity enforcement method, which on benchmark datasets, leads to greater predictive efficiency than the currently published method.
Matthew Prior, Terry Windeatt
Added 20 May 2010
Updated 20 May 2010
Type Conference
Year 2009
Where MCS
Authors Matthew Prior, Terry Windeatt
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