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.
Available under License : See the attached licence file.
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 (Paper)|
|Divisions :||Faculty of Engineering and Physical Sciences > Electronic Engineering > Centre for Vision Speech and Signal Processing|
|Uncontrolled Keywords :||FORESTS|
|Additional Information :||The original publication is available at http://www.springerlink.com|
|Depositing User :||Symplectic Elements|
|Date Deposited :||17 Feb 2012 12:10|
|Last Modified :||23 Sep 2013 19:03|
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