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Improved Uniformity Enforcement in Stochastic Discrimination

Prior, M and Windeatt, T (2009) Improved Uniformity Enforcement in Stochastic Discrimination In: 8th International Workshop on Multiple Classifier Systems, 2009-06-10 - 2009-06-12, Univ Iceland, Reykjavik, ICELAND.

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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.

Item Type: Conference or Workshop Item (Conference Paper)
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering > Centre for Vision Speech and Signal Processing
Authors :
Prior, M
Windeatt, T
Date : 2009
Contributors :
ContributionNameEmailORCID, JA, J, F BERLIN,
Uncontrolled Keywords : FORESTS
Additional Information : The original publication is available at
Depositing User : Symplectic Elements
Date Deposited : 17 Feb 2012 12:10
Last Modified : 31 Oct 2017 14:20

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