In statistical pattern recognition, class conditional probability distribution is estimated and used for classification. Since it is impossible to estimate the true distribution, usually the distribution is assumed to be a certain parametric model like normal distribution and the parameters that represent the distribution are estimated from training data. However there is no guarantee that the model is appropriate for the given data. In this paper, we propose a method to improve classification accuracy by transforming the distribution of the given data closer to the normal distribution using data transformation. We show how to modify the traditional quadratic discriminant function (QDF) in order to deal with the transformed data. Finally, we present some properties of the transformation and show the effectiveness of the proposed method through experiments with public databases.
|Number of pages||4|
|Journal||Proceedings - International Conference on Pattern Recognition|
|Publication status||Published - 2002|
ASJC Scopus subject areas
- Computer Vision and Pattern Recognition